Lead Data Engineer Manchester Hybrid Circa £85K An established organisation is looking for a hands-on Lead Data Engineer to take ownership of its existing data estate and lead the development of a modern, scalable data platform. This is an excellent opportunity to build a platform supporting Business Intelligence, Data Science and Applied AI. You'll introduce modern batch and real-time data-processing capabilities, automate inefficient processes and make trusted data more accessible across the organisation. Alongside remaining technically hands-on, you'll manage and develop a small team of Data Engineers. Key responsibilities Own the reliability, security and ongoing development of the data platform. Modernise the existing SQL Server, SSIS/SSDT and Power BI environment. Develop scalable batch and real-time data-processing solutions. Lead the design of a Customer Data Platform. Build and evolve a lakehouse architecture using AWS technologies. Create governed semantic models, consistent metrics and clear data definitions. Enable secure, self-service data analysis using modern AI tooling. Automate manual, repetitive or fragile data processes. Support BI, Data Science, Machine Learning and Applied AI teams. Take ownership of data governance, security, monitoring and platform costs. Line-manage, develop and help grow the Data Engineering team. Work closely with technical, operational and senior business stakeholders. Skills and experience We're looking for an experienced Data Engineer who has built and operated complete data platforms rather than solely delivering individual components. You'll need experience across: Batch data processing, ELT and orchestration. Modern streaming technologies such as Flink or comparable platforms. AWS data services, including S3, Lambda, Glue, Athena, Kinesis and RDS. Infrastructure as code using Terraform. CI/CD and automated testing. Lakehouse architecture and open table formats such as Iceberg. Medallion architecture and dimensional data modelling. Strong SQL and Python. SQL Server, SSIS/SSDT and Power BI. Semantic layers, common metrics and data glossaries. Data governance, lineage, retention, classification and auditability. Managing and developing a small technical team. You should also be comfortable using advanced AI coding assistants, such as Claude Code or equivalent tools, to improve engineering productivity while maintaining high standards of quality, governance and security. Desirable experience Regulated environment. PySpark for large-scale data transformations. Migrating legacy data estates to lakehouse or metadata-driven architectures. Customer Data Platforms or event-driven systems. Data-quality tools such as dbt tests or Great Expectations. MCP servers or governed natural-language data interfaces. Financial crime, fraud or regulatory-reporting data. Power BI administration and semantic modelling. The opportunity This is a genuinely influential role where you'll own the data platform end to end and help shape how data and AI are used across the organisation. You'll have the scope to introduce modern technologies, remove unnecessary manual work and build a platform capable of supporting future products, analytics and business decision-making. Salary: Circa £85K Location: Manchester Working pattern: Hybrid Lead Data Engineer - Manchester
Oct 06, 2026
Full time
Lead Data Engineer Manchester Hybrid Circa £85K An established organisation is looking for a hands-on Lead Data Engineer to take ownership of its existing data estate and lead the development of a modern, scalable data platform. This is an excellent opportunity to build a platform supporting Business Intelligence, Data Science and Applied AI. You'll introduce modern batch and real-time data-processing capabilities, automate inefficient processes and make trusted data more accessible across the organisation. Alongside remaining technically hands-on, you'll manage and develop a small team of Data Engineers. Key responsibilities Own the reliability, security and ongoing development of the data platform. Modernise the existing SQL Server, SSIS/SSDT and Power BI environment. Develop scalable batch and real-time data-processing solutions. Lead the design of a Customer Data Platform. Build and evolve a lakehouse architecture using AWS technologies. Create governed semantic models, consistent metrics and clear data definitions. Enable secure, self-service data analysis using modern AI tooling. Automate manual, repetitive or fragile data processes. Support BI, Data Science, Machine Learning and Applied AI teams. Take ownership of data governance, security, monitoring and platform costs. Line-manage, develop and help grow the Data Engineering team. Work closely with technical, operational and senior business stakeholders. Skills and experience We're looking for an experienced Data Engineer who has built and operated complete data platforms rather than solely delivering individual components. You'll need experience across: Batch data processing, ELT and orchestration. Modern streaming technologies such as Flink or comparable platforms. AWS data services, including S3, Lambda, Glue, Athena, Kinesis and RDS. Infrastructure as code using Terraform. CI/CD and automated testing. Lakehouse architecture and open table formats such as Iceberg. Medallion architecture and dimensional data modelling. Strong SQL and Python. SQL Server, SSIS/SSDT and Power BI. Semantic layers, common metrics and data glossaries. Data governance, lineage, retention, classification and auditability. Managing and developing a small technical team. You should also be comfortable using advanced AI coding assistants, such as Claude Code or equivalent tools, to improve engineering productivity while maintaining high standards of quality, governance and security. Desirable experience Regulated environment. PySpark for large-scale data transformations. Migrating legacy data estates to lakehouse or metadata-driven architectures. Customer Data Platforms or event-driven systems. Data-quality tools such as dbt tests or Great Expectations. MCP servers or governed natural-language data interfaces. Financial crime, fraud or regulatory-reporting data. Power BI administration and semantic modelling. The opportunity This is a genuinely influential role where you'll own the data platform end to end and help shape how data and AI are used across the organisation. You'll have the scope to introduce modern technologies, remove unnecessary manual work and build a platform capable of supporting future products, analytics and business decision-making. Salary: Circa £85K Location: Manchester Working pattern: Hybrid Lead Data Engineer - Manchester
Junior Data Engineer Location: Mainly remote, with occasional meetings in London Rate: Up to £395 per day Clearance: SC cleared preferred - must be eligible for SC Sector: Central Government Type: Contract The Role We're looking for a Junior Data Engineer to join a long-term central government data programme. This is a hands-on role supporting the movement, transformation and processing of operational data into a large-scale data platform. You'll work as part of an established engineering team, building and maintaining data pipelines and supporting data solutions in production. Key Responsibilities Build and maintain data pipelines Support data ingestion, transformation and processing Work with large and complex datasets Develop solutions using Python and/or Java Use SQL to query, manipulate and transform data Work with cloud-based data platforms Troubleshoot pipeline and data issues Support solutions through development and into production Collaborate with engineers and technical teams across the programme Skills & Experience Commercial Data Engineering or data-focused software engineering experience Python and/or Java Strong SQL skills Experience building or maintaining data pipelines Experience with cloud-based data platforms Good understanding of production engineering environments Experience with any of the following would be beneficial: AWS, Kafka, Kubernetes, Spark, Glue, Athena, Redshift, S3 or Lake Formation. You don't need experience with every technology listed. We're looking for strong engineering fundamentals, hands-on ability and experience working with data in production environments. Previous experience within central government, policing, national security or another complex public sector environment would be beneficial. Security Clearance Existing SC clearance is preferred, although candidates who are eligible to obtain SC clearance will also be considered.
Oct 05, 2026
Full time
Junior Data Engineer Location: Mainly remote, with occasional meetings in London Rate: Up to £395 per day Clearance: SC cleared preferred - must be eligible for SC Sector: Central Government Type: Contract The Role We're looking for a Junior Data Engineer to join a long-term central government data programme. This is a hands-on role supporting the movement, transformation and processing of operational data into a large-scale data platform. You'll work as part of an established engineering team, building and maintaining data pipelines and supporting data solutions in production. Key Responsibilities Build and maintain data pipelines Support data ingestion, transformation and processing Work with large and complex datasets Develop solutions using Python and/or Java Use SQL to query, manipulate and transform data Work with cloud-based data platforms Troubleshoot pipeline and data issues Support solutions through development and into production Collaborate with engineers and technical teams across the programme Skills & Experience Commercial Data Engineering or data-focused software engineering experience Python and/or Java Strong SQL skills Experience building or maintaining data pipelines Experience with cloud-based data platforms Good understanding of production engineering environments Experience with any of the following would be beneficial: AWS, Kafka, Kubernetes, Spark, Glue, Athena, Redshift, S3 or Lake Formation. You don't need experience with every technology listed. We're looking for strong engineering fundamentals, hands-on ability and experience working with data in production environments. Previous experience within central government, policing, national security or another complex public sector environment would be beneficial. Security Clearance Existing SC clearance is preferred, although candidates who are eligible to obtain SC clearance will also be considered.
Data Engineer Trading Platform Remote Up to £200k Plexus is working with a client in the DeFi trading space. They are a permission less, on-chain trading protocol allowing anyone to trade stocks, commodities, currencies and crypto through a fully transparent, auditable stack. They have raised $30M+ from a strong group of institutional and strategic backers. They are looking for a Data Engineer to own their monitoring, parameter systems and data infrastructure as the protocol scales. Role: Architecture and maintaining ingestion and transformation pipelines for on-chain events and market data. Implementing parameter models as reproducible, versioned code for review and deployment. Designing Grafana dashboards and alerting for protocol health and economic correctness. Building back testing and replay frameworks to validate parameter changes under stress scenarios. Requirements: Strong Python and SQL experience building production data pipelines. Hands-on AWS experience across S3, Athena/Glue, Redshift and ECS/Lambda. Proven ability to build monitoring systems with Grafana, including alerting and incident workflows. Experience productionising research logic with CI/CD, testing and reproducible environments. Benefits: Competitive compensation package. Flexible work arrangements. Professional development opportunities. If you're interested, please hit the "Easy Apply" button or get in touch
Sep 30, 2026
Full time
Data Engineer Trading Platform Remote Up to £200k Plexus is working with a client in the DeFi trading space. They are a permission less, on-chain trading protocol allowing anyone to trade stocks, commodities, currencies and crypto through a fully transparent, auditable stack. They have raised $30M+ from a strong group of institutional and strategic backers. They are looking for a Data Engineer to own their monitoring, parameter systems and data infrastructure as the protocol scales. Role: Architecture and maintaining ingestion and transformation pipelines for on-chain events and market data. Implementing parameter models as reproducible, versioned code for review and deployment. Designing Grafana dashboards and alerting for protocol health and economic correctness. Building back testing and replay frameworks to validate parameter changes under stress scenarios. Requirements: Strong Python and SQL experience building production data pipelines. Hands-on AWS experience across S3, Athena/Glue, Redshift and ECS/Lambda. Proven ability to build monitoring systems with Grafana, including alerting and incident workflows. Experience productionising research logic with CI/CD, testing and reproducible environments. Benefits: Competitive compensation package. Flexible work arrangements. Professional development opportunities. If you're interested, please hit the "Easy Apply" button or get in touch
Slalom is a global, human-centric business and technology consulting firm. We specialise in partnering with organisations that aspire to excellence, helping them tackle complex challenges and achieve transformative results through strategy, technology, and business transformation services. By prioritising people, Slalom creates a unique consulting experience, with a team of strategists and engineers delivering practical, end-to-end solutions that drive impactful outcomes for our clients. We empower people and organisations to dream bigger, move faster, and build better tomorrows for all. Slalom's Data Capability At Slalom, we believe that through our trusted relationships with our clients, we can create modern data solutions that drive results and improve the world. Interested in Strategy? Have a passion for Architecture? Want to work in a team that is pushing the forefront of the latest technology in the Engineering and AI space? We can offer you this. Slalom is agnostic when it comes to the technology we work with and we support clients in a range of data cloud partnershipsincludingAWS, Azure, Snowflake and Databricks to name but a few. We are interested in individuals who are passionate and curious about what is next. As a Senior Consultant AWS Data & AI Engineer at Slalom, you areaskilled engineerresponsible for delivering high-quality data solutions that generate measurable business value. Youdemonstratedeep technicalexpertisein AWS data services, combined with a keen understanding of client needs and the ability to collaborate productively within teams. Additionally, you play a key role in advancing our Data & Technology Capability. This position requiresproficiencyin designing and constructing robust data platforms, alongside the capacity to foster strong client partnerships. What will you do? Client Delivery & Technical Excellence Design, build, and implement scalable data engineering solutions on AWS, including data pipelines, ETL/ELT processes, and data integration frameworks. Develop andoptimizedata architectures using AWS services such as S3, Glue, Lambda, Redshift, Kinesis, EMR, Amazon OpenSearch Service, and graph databases such as Amazon Neptune, and related technologies. Ensure solutions follow AWS best practices for security, performance, cost optimization, and operational excellence. Collaborate with data architects, analysts, and business stakeholders to translate requirements into technical implementations. Use AI-assisted development tools appropriately to support engineering productivity, code quality, testing, and delivery. Mentor junior team members and contribute to building technical capability within project teams. Client Advisory & Relationship Building Act as a trusted advisor to client stakeholders, understanding their business challenges and recommendingappropriate datasolutions. Communicate technical concepts clearly to both technical and non-technical audiences. Contribute to client workshops, requirements gathering sessions, and solution design activities. Practice Development & Knowledge Sharing Stay current with AWS data engineering trends, services, and best practices. Contribute to the development of Slalom's data engineering accelerators, frameworks, and methodologies. Share knowledge through internal presentations, documentation, and mentoring. Participate in Slalom's learning culture and pursue continuous professional development. Whatyou'llbring Considerable experiencein data engineeringfocused on AWS data platforms and services. Strong hands-on experience withAWS data servicesincluding S3, Glue, Lambda, Redshift, Athena, EMR, Kinesis, Amazon OpenSearch Service, and graph databases such as Amazon Neptune, and related technologies. Proficiencyin programming languages such asPythonandSQL fordata processing and transformation. Experience designing and implementingETL/ELT pipelines, data integration patterns, and workflow orchestration. Understanding ofdata modelling concepts(dimensional modelling, data vault, normalized schemas) and when to apply them. Knowledge ofdata governance, data quality, and metadata managementprinciples. Experience withInfrastructure as Code(CloudFormation, Terraform, CDK) and CI/CD practices. Strong problem-solving skills and ability to work effectively in fast-paced consultingenvironments. Excellent communication and interpersonal skills, withdemonstratedability to work collaboratively with diverse teams. Client-facing consulting experiencewith ability to build rapport and credibility with stakeholders. AWS certificationssuch as AWS Certified Data Engineer - Associate, AWS Certified Solutions Architect - Associate, or AWS Certified Developer - Associate Experience withstreaming data architecturesand real-time analytics Familiaritywithdata platformssuch asSnowflake, Databricks is a bonus Experience with ML/AI engineering, including building or integrating production-grade machine learning or generative AI solutions. Experience with agentic development patterns, including tool use, workflow orchestration, and evaluation of agent behavior. Experience with semantic layers and ontology design, including defining consistent business concepts, relationships, and metadata for analytics and AI use cases. Relevant AWS AI certifications, such as AWS Certified AI Practitioner, AWS Certified Machine Learning Engineer - Associate, or AWS Certified Generative AI Developer - Professional. We have a question for you - and it's something we're passionate about. Can you imagine a world in which you can truly love your life and your work? Well, we have some good news - creating that world and making this vision a reality is what we get out of bed for; it's our north star. But what do we really mean when we talk about loving your life and your work? Let's look at the ways in which we help our team members to achieve this - and the 'how'. Deep connections, better outcome We have deep relationships with leading technology partners and they love us for our innovative and outcome based approach. Our people are passionate about solving our clients problems using the tech that's the best solution for them. What's more, we're there to work side-by-side with our client teams to enable them for success long after we've gone. We're all about momentum that outlasts us. Flexibility Life is busy, and we understand that. Our team often juggles work, family, personal commitments, and crucial client obligations. We prioritise supporting our people in balancing what matters to them while ensuring we meet our client commitments. Flexibility is key, as we sometimes need to adapt to meet client needs. This way, our team can work on high-impact projects they'll love, knowing they have the flexibility to manage their personal and professional lives effectively. People-first Great solutions start with great people. At our employee-led company, your voice matters, and we love to hear your feedback. By leading with kindness, empathy, and striving for equity, we create better experiences for everyone. Our culture encourages passion, adventure, adaptability, and diverse thinking. Inclusion, diversity, and equity are our top priorities, empowering our team to be their best selves. Together, we can create a great future by working collaboratively and supporting each other. Rewards There's no shying away from it - the compensation and benefits on offer have to be competitive too, right? We know that. That's why we have a dedicated team working with our leaders to ensure our packages are fair, competitive, and rewarding! Who are we We are Slalom! Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries partner with clients to co-create powerful customer experiences, modern ways of working and meaningful impact. What sets us apart? We believe work should be challenging and fulfilling, not perfect, but possible. That's why we prioritise purpose, flexibility, connection and recognition so our people can thrive and love what they do, most days. We're honoured to be consistently recognised as a great place to work, including being one of Fortune's 100 Best Companies to Work for nine years running. Read more at Slalom prides itself on helping our team members thrive in their work and life. As a result, Slalom is proud to invest in our team members with competitive and innovative benefit programs and services that meet the unique needs of our diverse workforce. If you require any assistance with regards to reasonable adjustments during the recruitment process, please do not hesitate to contact us - we will always be happy to help.
Sep 30, 2026
Full time
Slalom is a global, human-centric business and technology consulting firm. We specialise in partnering with organisations that aspire to excellence, helping them tackle complex challenges and achieve transformative results through strategy, technology, and business transformation services. By prioritising people, Slalom creates a unique consulting experience, with a team of strategists and engineers delivering practical, end-to-end solutions that drive impactful outcomes for our clients. We empower people and organisations to dream bigger, move faster, and build better tomorrows for all. Slalom's Data Capability At Slalom, we believe that through our trusted relationships with our clients, we can create modern data solutions that drive results and improve the world. Interested in Strategy? Have a passion for Architecture? Want to work in a team that is pushing the forefront of the latest technology in the Engineering and AI space? We can offer you this. Slalom is agnostic when it comes to the technology we work with and we support clients in a range of data cloud partnershipsincludingAWS, Azure, Snowflake and Databricks to name but a few. We are interested in individuals who are passionate and curious about what is next. As a Senior Consultant AWS Data & AI Engineer at Slalom, you areaskilled engineerresponsible for delivering high-quality data solutions that generate measurable business value. Youdemonstratedeep technicalexpertisein AWS data services, combined with a keen understanding of client needs and the ability to collaborate productively within teams. Additionally, you play a key role in advancing our Data & Technology Capability. This position requiresproficiencyin designing and constructing robust data platforms, alongside the capacity to foster strong client partnerships. What will you do? Client Delivery & Technical Excellence Design, build, and implement scalable data engineering solutions on AWS, including data pipelines, ETL/ELT processes, and data integration frameworks. Develop andoptimizedata architectures using AWS services such as S3, Glue, Lambda, Redshift, Kinesis, EMR, Amazon OpenSearch Service, and graph databases such as Amazon Neptune, and related technologies. Ensure solutions follow AWS best practices for security, performance, cost optimization, and operational excellence. Collaborate with data architects, analysts, and business stakeholders to translate requirements into technical implementations. Use AI-assisted development tools appropriately to support engineering productivity, code quality, testing, and delivery. Mentor junior team members and contribute to building technical capability within project teams. Client Advisory & Relationship Building Act as a trusted advisor to client stakeholders, understanding their business challenges and recommendingappropriate datasolutions. Communicate technical concepts clearly to both technical and non-technical audiences. Contribute to client workshops, requirements gathering sessions, and solution design activities. Practice Development & Knowledge Sharing Stay current with AWS data engineering trends, services, and best practices. Contribute to the development of Slalom's data engineering accelerators, frameworks, and methodologies. Share knowledge through internal presentations, documentation, and mentoring. Participate in Slalom's learning culture and pursue continuous professional development. Whatyou'llbring Considerable experiencein data engineeringfocused on AWS data platforms and services. Strong hands-on experience withAWS data servicesincluding S3, Glue, Lambda, Redshift, Athena, EMR, Kinesis, Amazon OpenSearch Service, and graph databases such as Amazon Neptune, and related technologies. Proficiencyin programming languages such asPythonandSQL fordata processing and transformation. Experience designing and implementingETL/ELT pipelines, data integration patterns, and workflow orchestration. Understanding ofdata modelling concepts(dimensional modelling, data vault, normalized schemas) and when to apply them. Knowledge ofdata governance, data quality, and metadata managementprinciples. Experience withInfrastructure as Code(CloudFormation, Terraform, CDK) and CI/CD practices. Strong problem-solving skills and ability to work effectively in fast-paced consultingenvironments. Excellent communication and interpersonal skills, withdemonstratedability to work collaboratively with diverse teams. Client-facing consulting experiencewith ability to build rapport and credibility with stakeholders. AWS certificationssuch as AWS Certified Data Engineer - Associate, AWS Certified Solutions Architect - Associate, or AWS Certified Developer - Associate Experience withstreaming data architecturesand real-time analytics Familiaritywithdata platformssuch asSnowflake, Databricks is a bonus Experience with ML/AI engineering, including building or integrating production-grade machine learning or generative AI solutions. Experience with agentic development patterns, including tool use, workflow orchestration, and evaluation of agent behavior. Experience with semantic layers and ontology design, including defining consistent business concepts, relationships, and metadata for analytics and AI use cases. Relevant AWS AI certifications, such as AWS Certified AI Practitioner, AWS Certified Machine Learning Engineer - Associate, or AWS Certified Generative AI Developer - Professional. We have a question for you - and it's something we're passionate about. Can you imagine a world in which you can truly love your life and your work? Well, we have some good news - creating that world and making this vision a reality is what we get out of bed for; it's our north star. But what do we really mean when we talk about loving your life and your work? Let's look at the ways in which we help our team members to achieve this - and the 'how'. Deep connections, better outcome We have deep relationships with leading technology partners and they love us for our innovative and outcome based approach. Our people are passionate about solving our clients problems using the tech that's the best solution for them. What's more, we're there to work side-by-side with our client teams to enable them for success long after we've gone. We're all about momentum that outlasts us. Flexibility Life is busy, and we understand that. Our team often juggles work, family, personal commitments, and crucial client obligations. We prioritise supporting our people in balancing what matters to them while ensuring we meet our client commitments. Flexibility is key, as we sometimes need to adapt to meet client needs. This way, our team can work on high-impact projects they'll love, knowing they have the flexibility to manage their personal and professional lives effectively. People-first Great solutions start with great people. At our employee-led company, your voice matters, and we love to hear your feedback. By leading with kindness, empathy, and striving for equity, we create better experiences for everyone. Our culture encourages passion, adventure, adaptability, and diverse thinking. Inclusion, diversity, and equity are our top priorities, empowering our team to be their best selves. Together, we can create a great future by working collaboratively and supporting each other. Rewards There's no shying away from it - the compensation and benefits on offer have to be competitive too, right? We know that. That's why we have a dedicated team working with our leaders to ensure our packages are fair, competitive, and rewarding! Who are we We are Slalom! Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries partner with clients to co-create powerful customer experiences, modern ways of working and meaningful impact. What sets us apart? We believe work should be challenging and fulfilling, not perfect, but possible. That's why we prioritise purpose, flexibility, connection and recognition so our people can thrive and love what they do, most days. We're honoured to be consistently recognised as a great place to work, including being one of Fortune's 100 Best Companies to Work for nine years running. Read more at Slalom prides itself on helping our team members thrive in their work and life. As a result, Slalom is proud to invest in our team members with competitive and innovative benefit programs and services that meet the unique needs of our diverse workforce. If you require any assistance with regards to reasonable adjustments during the recruitment process, please do not hesitate to contact us - we will always be happy to help.
AWS Data Engineer - Contract (Outside IR35) Location: Oxford Working Patterns: Hybrid - 2 days on site, per week Contract Duration: 6 months, extension likely Opus have partnered with a leading manufacturer who're seeking an experienced AWS Data Engineer to help to design, build and optimise data pipelines to support business reporting, analytics and decision making. You'll be working closely with engineering and technology stakeholders to help in the delivery of reliable data solutions. The Role: This one is heavily focused on development and the maintenance of cloud-based data infrastructure. There is a strong focus on AWS Services, Data Modelling, and Pipeline Performance. This role will suit a candidate who's comfortable working with messy data and turning real-world data into trusted insights. Responsibilities: - Design & build scalable data pipelines of AWS - Ingest, transform and model data from multiple source systems - Work with S3, Glue, Lambda, Athena, Redshift and related AWS Services - Support CI/CD and infrastructure as a code Requirements: - Must have strong, commercial AWS (contract) Data Engineering experience - Must bring solid knowledge of data warehousing and modelling - Experience with Airflow and infrastructure as a code (Terraform, CloudFormation) would be beneficial - Must have experience with Redshift Nice to have: - Experience with dbt / lake formation - One or more AWS Certifications Applications are now open! - Reach out to Adam Akhtar at Opus for further information.
Sep 28, 2026
Contractor
AWS Data Engineer - Contract (Outside IR35) Location: Oxford Working Patterns: Hybrid - 2 days on site, per week Contract Duration: 6 months, extension likely Opus have partnered with a leading manufacturer who're seeking an experienced AWS Data Engineer to help to design, build and optimise data pipelines to support business reporting, analytics and decision making. You'll be working closely with engineering and technology stakeholders to help in the delivery of reliable data solutions. The Role: This one is heavily focused on development and the maintenance of cloud-based data infrastructure. There is a strong focus on AWS Services, Data Modelling, and Pipeline Performance. This role will suit a candidate who's comfortable working with messy data and turning real-world data into trusted insights. Responsibilities: - Design & build scalable data pipelines of AWS - Ingest, transform and model data from multiple source systems - Work with S3, Glue, Lambda, Athena, Redshift and related AWS Services - Support CI/CD and infrastructure as a code Requirements: - Must have strong, commercial AWS (contract) Data Engineering experience - Must bring solid knowledge of data warehousing and modelling - Experience with Airflow and infrastructure as a code (Terraform, CloudFormation) would be beneficial - Must have experience with Redshift Nice to have: - Experience with dbt / lake formation - One or more AWS Certifications Applications are now open! - Reach out to Adam Akhtar at Opus for further information.
Contract Data Engineer / Outside IR35 / Hybrid (one day a week North of Leeds) / (Apply online only) per day We are supporting a major data transformation programme and are looking for an experienced Senior AWS Data Engineer to help deliver the migration of a legacy SQL-based data warehouse into a modern, cloud-native AWS data platform. You'll play a key role in designing and building scalable data solutions, establishing a strategic source of truth for reporting and analytics, and helping to shape the future of the organisation's data capabilities. Key Responsibilities Design and develop scalable AWS-based data lake and data warehouse solutions Build and optimise ETL/ELT pipelines using Python and SQL Migrate data from legacy SQL environments into a cloud-native architecture Essential Skills SQL and Python Hands-on experience with: Amazon S3 AWS Glue Amazon Redshift AWS Lambda Athena Data warehouse and data lake design experience ETL/ELT development and optimisation Data migration and integration expertise If this role is of interest, please apply with an up to date CV.
Sep 28, 2026
Contractor
Contract Data Engineer / Outside IR35 / Hybrid (one day a week North of Leeds) / (Apply online only) per day We are supporting a major data transformation programme and are looking for an experienced Senior AWS Data Engineer to help deliver the migration of a legacy SQL-based data warehouse into a modern, cloud-native AWS data platform. You'll play a key role in designing and building scalable data solutions, establishing a strategic source of truth for reporting and analytics, and helping to shape the future of the organisation's data capabilities. Key Responsibilities Design and develop scalable AWS-based data lake and data warehouse solutions Build and optimise ETL/ELT pipelines using Python and SQL Migrate data from legacy SQL environments into a cloud-native architecture Essential Skills SQL and Python Hands-on experience with: Amazon S3 AWS Glue Amazon Redshift AWS Lambda Athena Data warehouse and data lake design experience ETL/ELT development and optimisation Data migration and integration expertise If this role is of interest, please apply with an up to date CV.
Data Engineer We are looking for an experienced Data Engineer to join a major data transformation programme on an initial 3-month contract. This is a hands-on role for someone who enjoys building robust, scalable data platforms and pipelines. You will work as part of a wider technical delivery team, helping to develop a modern AWS-based data environment that supports enterprise reporting, analytics and operational decision-making. The successful candidate will ideally be able to attend the Leeds office one day per week. However, flexible arrangements can be discussed for the right person. The role You will play a key part in designing, building and improving cloud-based data solutions. This will include developing data pipelines, integrating data from multiple systems and helping to create trusted, well-governed datasets for reporting and analytics. You will work closely with Data Architects, Analysts, Developers and other technical stakeholders to turn business and technical requirements into practical, scalable solutions. Key responsibilities Design and build scalable data lake and data warehouse solutions on AWS. Develop data ingestion, transformation and storage pipelines to support reporting, analytics and operational use cases. Work with AWS services including S3, Redshift, Glue, Lambda, Lake Formation, Athena and EventBridge. Implement Bronze, Silver and Gold data layers using Medallion Architecture principles. Build and maintain ETL/ELT pipelines across batch, streaming and Change Data Capture (CDC) workloads. Develop reusable transformation frameworks, data models and curated datasets using Python, SQL and dbt. Support data migration and integration activity across a range of source and target systems. Build integrations using APIs, messaging technologies and file-based ingestion methods. Implement data validation, reconciliation and error-handling processes to improve data quality. Support data governance, lineage, security and audit requirements. Help automate deployments and infrastructure using Terraform or CloudFormation. Contribute to CI/CD pipelines using GitHub Actions or similar tooling. Improve monitoring and operational support using tools such as CloudWatch and Splunk. Produce clear technical documentation for pipelines, mappings, transformations and data flows. Promote good engineering practice, automation, standardisation and continuous improvement. About you You will be a capable, delivery-focused Data Engineer with experience working on modern cloud data platforms. You should be comfortable working independently, but equally happy collaborating with a multidisciplinary technical team. You will have strong experience with AWS data services and be confident designing and developing data pipelines in complex transformation, migration or integration environments. Key skills and experience Proven experience designing and delivering AWS-based data platforms. Strong hands-on experience with services such as S3, Redshift, Glue, Lambda, Lake Formation, Athena and EventBridge. Experience implementing Medallion Architecture, including Bronze, Silver and Gold data layers. Strong data engineering experience across batch, streaming and CDC ingestion patterns. Advanced SQL skills and strong Python development experience. Experience using dbt for data transformation and modelling would be highly beneficial. Experience building and optimising ETL/ELT pipelines. Good understanding of data warehousing, dimensional modelling and analytical data structures. Experience with data integration, APIs, messaging and file-based data ingestion. Knowledge of Terraform or CloudFormation for Infrastructure as Code. Experience with CI/CD tooling, ideally GitHub Actions. Experience with monitoring, observability and data quality frameworks. Knowledge of CloudWatch, Splunk or similar monitoring tools. An understanding of data governance, lineage, validation and security principles. Strong communication skills and the ability to work with both technical and non-technical stakeholders. This is a fast-moving requirement, and we are keen to identify suitable candidates quickly. Please get in touch with relevant profiles.
Sep 27, 2026
Contractor
Data Engineer We are looking for an experienced Data Engineer to join a major data transformation programme on an initial 3-month contract. This is a hands-on role for someone who enjoys building robust, scalable data platforms and pipelines. You will work as part of a wider technical delivery team, helping to develop a modern AWS-based data environment that supports enterprise reporting, analytics and operational decision-making. The successful candidate will ideally be able to attend the Leeds office one day per week. However, flexible arrangements can be discussed for the right person. The role You will play a key part in designing, building and improving cloud-based data solutions. This will include developing data pipelines, integrating data from multiple systems and helping to create trusted, well-governed datasets for reporting and analytics. You will work closely with Data Architects, Analysts, Developers and other technical stakeholders to turn business and technical requirements into practical, scalable solutions. Key responsibilities Design and build scalable data lake and data warehouse solutions on AWS. Develop data ingestion, transformation and storage pipelines to support reporting, analytics and operational use cases. Work with AWS services including S3, Redshift, Glue, Lambda, Lake Formation, Athena and EventBridge. Implement Bronze, Silver and Gold data layers using Medallion Architecture principles. Build and maintain ETL/ELT pipelines across batch, streaming and Change Data Capture (CDC) workloads. Develop reusable transformation frameworks, data models and curated datasets using Python, SQL and dbt. Support data migration and integration activity across a range of source and target systems. Build integrations using APIs, messaging technologies and file-based ingestion methods. Implement data validation, reconciliation and error-handling processes to improve data quality. Support data governance, lineage, security and audit requirements. Help automate deployments and infrastructure using Terraform or CloudFormation. Contribute to CI/CD pipelines using GitHub Actions or similar tooling. Improve monitoring and operational support using tools such as CloudWatch and Splunk. Produce clear technical documentation for pipelines, mappings, transformations and data flows. Promote good engineering practice, automation, standardisation and continuous improvement. About you You will be a capable, delivery-focused Data Engineer with experience working on modern cloud data platforms. You should be comfortable working independently, but equally happy collaborating with a multidisciplinary technical team. You will have strong experience with AWS data services and be confident designing and developing data pipelines in complex transformation, migration or integration environments. Key skills and experience Proven experience designing and delivering AWS-based data platforms. Strong hands-on experience with services such as S3, Redshift, Glue, Lambda, Lake Formation, Athena and EventBridge. Experience implementing Medallion Architecture, including Bronze, Silver and Gold data layers. Strong data engineering experience across batch, streaming and CDC ingestion patterns. Advanced SQL skills and strong Python development experience. Experience using dbt for data transformation and modelling would be highly beneficial. Experience building and optimising ETL/ELT pipelines. Good understanding of data warehousing, dimensional modelling and analytical data structures. Experience with data integration, APIs, messaging and file-based data ingestion. Knowledge of Terraform or CloudFormation for Infrastructure as Code. Experience with CI/CD tooling, ideally GitHub Actions. Experience with monitoring, observability and data quality frameworks. Knowledge of CloudWatch, Splunk or similar monitoring tools. An understanding of data governance, lineage, validation and security principles. Strong communication skills and the ability to work with both technical and non-technical stakeholders. This is a fast-moving requirement, and we are keen to identify suitable candidates quickly. Please get in touch with relevant profiles.
Principal Cloud Engineer (Terraform) London About the Role As a CBRE Systems Engineer Principal -AI & CLoud Engineer, you will be an embedded technical expert within the Cloud Engineering & FinOps team, working alongside fellow FinOps engineers to build, maintain, and continuously improve the data and AI/ML capabilities that power CBRE's cloud cost management practice. You bring deep, hands on expertise in data engineering and AI/ML, and you apply that expertise directly to FinOps problems - building the pipelines, models, and analytical tools that the team depends on every day. This role is a principal-level individual contributor. You are a highly skilled practitioner who goes deep on the most technically complex problems: designing high-quality data pipelines, developing and tuning AI/ML models, optimizing database performance, and translating raw multi-cloud billing data into reliable, actionable intelligence. You collaborate closely with FinOps analysts, cloud engineers, and platform leads to deliver engineering work that raises the quality and capability of the entire practice. What You'll Do Cloud Engineering & ETL/ELT Pipeline Development Build, maintain, and improve ELT/ETL pipelines that ingest billing, usage, and tagging data from AWS, Azure, and GCP into CBRE's centralized FinOps data store, applying modern pipeline patterns including event-driven ingestion, incremental loading, and change data capture. Design and implement layered data models and transformation logic (raw, conformed, aggregated) using tools such as dbt, Spark, and cloud-native processing services, ensuring data is clean, consistent, and ready for downstream consumption. Develop modular, reusable data transformation components and functions that can be shared across FinOps pipelines, reducing duplication and improving maintainability across the platform. Manage production pipelines end-to-end - orchestration, scheduling, dependency management, error handling, alerting, and incident response - to ensure reliable and timely data delivery for the FinOps team. Apply data quality and validation frameworks to ensure accuracy, completeness, and freshness of cost and usage data across all cloud providers; instrument pipelines with observability tooling to surface issues proactively. Build and maintain reusable data assets - curated datasets, aggregations, and data marts - that power FinOps dashboards, showback/chargeback reporting, and AI/ML model inputs. Microservices & Platform Feature Development Design and build modular microservices and APIs that expose FinOps data and AI/ML capabilities as reusable services - enabling other teams and internal platforms to consume cost intelligence programmatically. Contribute new features and capabilities to CBRE's FinOps platform, translating analyst and engineering requirements into well-structured, production-ready service components. Integrate data and AI services with internal platforms such as AIDP (Automated Infrastructure Deployment Platform) and ECMP (Enterprise Container Management Platform), embedding cost signals directly into existing engineering workflows. Follow software engineering best practices in all platform work: clean interfaces, unit testing, API versioning, containerization, and CI/CD deployment pipelines. Identify opportunities to refactor or modularize existing FinOps platform components, improving reliability, scalability, and ease of maintenance. AI/ML Model Development Develop, train, and evaluate machine learning models that address core FinOps use cases: cost anomaly detection, spend forecasting, workload rightsizing recommendations, and commitment coverage optimization. Implement and maintain MLOps pipelines for model versioning, automated retraining, performance monitoring, and drift detection as cloud usage patterns evolve. Experiment with and apply generative AI and LLM capabilities to FinOps workflows - such as natural language interfaces for cost querying, AI-assisted tagging remediation, or intelligent cost allocation suggestions. Collaborate with FinOps analysts to validate model outputs against business expectations, refining approaches iteratively based on real-world feedback. Document model design, feature engineering decisions, and evaluation results to ensure team-wide understanding and reproducibility. Quality, Governance & Best Practices Implement and uphold data governance standards: lineage tracking, cataloging, column-level security, and RBAC for sensitive cost and financial data. Follow and actively contribute to team engineering standards for code quality, testing, documentation, and CI/CD - raising the bar for the team's collective output through code reviews and knowledge sharing. Apply extensive and diversified knowledge of data engineering principles, advanced techniques, and theories to solve complex FinOps data challenges. Lead by example and model behaviors consistent with CBRE RISE values - Respect, Integrity, Service, and Excellence. What You'll Need Bachelor's Degree preferred relevant experience. In lieu of a degree, a combination of experience and education will be considered. Hands on data engineering experience in production environments: ELT/ETL pipelines, lakehouses, data warehouses, and large scale analytical workloads. Applied AI/ML engineering experience with a track record of delivering models into production. Software or platform engineering experience building APIs, microservices, or backend services in a cloud environment. Experience working with cloud billing data, FinOps tooling, or cloud cost management across at least one major cloud provider (Azure, AWS, or GCP). Technical Skills Data engineering: strong proficiency with Apache Spark, dbt, Airflow / Azure Data Factory or equivalent orchestration, Data Lake / Apache Iceberg, and advanced SQL for large scale transformations. Microservices & APIs: experience building modular, production grade microservices and REST APIs using Python (FastAPI, Flask, or equivalent); understanding of service design principles including versioning, fault tolerance, and observability. Cloud platforms: working knowledge of cost and usage data schemas across Azure (Cost Management, Synapse / Fabric), AWS (Cost Explorer, Athena, Glue), and GCP (BigQuery). AI/ML: proficiency in the Python ML ecosystem (scikit learn, XGBoost, Prophet), experience building and deploying models, and familiarity with MLflow or equivalent MLOps tooling. Generative AI: practical experience with LLM APIs (Azure OpenAI, OpenAI, or equivalent) for building intelligent interfaces or automating classification, tagging, and cost attribution tasks. Software engineering fundamentals: Python, SQL, Git, CI/CD, containerization (Docker / Kubernetes), and infrastructure as code familiarity (Terraform or Bicep). Data governance: experience with data cataloging, lineage, access controls, and data quality frameworks in production data platforms. Competencies In-depth expertise in leading edge data engineering and AI/ML techniques and technologies, with the ability to identify and solve complex technical problems independently. Multi dimensional, conceptual, and innovative thinking - able to design creative solutions to FinOps data and AI challenges without direct supervision. Strong collaboration and communication skills - comfortable working alongside engineers and analysts at all levels, explaining technical work clearly to non technical stakeholders. Detail oriented approach to data quality, model reliability, and engineering rigor - takes ownership of the accuracy and dependability of the work delivered. Expert organizational skills with an unrivaled inquisitive mindset. In depth knowledge of Microsoft Office products including Word, Excel, and Outlook. Preferred Qualifications FinOps Certified Practitioner (FOCP) designation from the FinOps Foundation. Cloud data or AI certifications (e.g., Azure Data Engineer Associate, AWS Data Engineer Associate, GCP Professional Data Engineer, or equivalent ML/AI certifications). Experience with real time or near real time cost event streaming and alerting pipelines. Familiarity with FinOps Foundation framework concepts including the Inform, Optimize, and Operate phases. Why CBRE When you join CBRE, you become part of the global leader in commercial real estate services and investment that helps businesses and people thrive. We are dynamic problem solvers and forward thinking professionals who create significant impact. Our collaborative culture is built on our shared values - respect, integrity, service and excellence - and we value the diverse perspectives, backgrounds and skillsets of our people. At CBRE, you have the opportunity to realize your full potential. Our Values in Hiring At CBRE, we are committed to fostering a culture where everyone feels they belong. We value diverse perspectives and experiences, and we welcome all applications. Applicant AI Use Disclosure We value human interaction to understand each candidate's unique experience, skills and aspirations. We do not use artificial intelligence (AI) tools to make hiring decisions, and we ask that candidates disclose any use of AI in the application and interview process.
Sep 23, 2026
Full time
Principal Cloud Engineer (Terraform) London About the Role As a CBRE Systems Engineer Principal -AI & CLoud Engineer, you will be an embedded technical expert within the Cloud Engineering & FinOps team, working alongside fellow FinOps engineers to build, maintain, and continuously improve the data and AI/ML capabilities that power CBRE's cloud cost management practice. You bring deep, hands on expertise in data engineering and AI/ML, and you apply that expertise directly to FinOps problems - building the pipelines, models, and analytical tools that the team depends on every day. This role is a principal-level individual contributor. You are a highly skilled practitioner who goes deep on the most technically complex problems: designing high-quality data pipelines, developing and tuning AI/ML models, optimizing database performance, and translating raw multi-cloud billing data into reliable, actionable intelligence. You collaborate closely with FinOps analysts, cloud engineers, and platform leads to deliver engineering work that raises the quality and capability of the entire practice. What You'll Do Cloud Engineering & ETL/ELT Pipeline Development Build, maintain, and improve ELT/ETL pipelines that ingest billing, usage, and tagging data from AWS, Azure, and GCP into CBRE's centralized FinOps data store, applying modern pipeline patterns including event-driven ingestion, incremental loading, and change data capture. Design and implement layered data models and transformation logic (raw, conformed, aggregated) using tools such as dbt, Spark, and cloud-native processing services, ensuring data is clean, consistent, and ready for downstream consumption. Develop modular, reusable data transformation components and functions that can be shared across FinOps pipelines, reducing duplication and improving maintainability across the platform. Manage production pipelines end-to-end - orchestration, scheduling, dependency management, error handling, alerting, and incident response - to ensure reliable and timely data delivery for the FinOps team. Apply data quality and validation frameworks to ensure accuracy, completeness, and freshness of cost and usage data across all cloud providers; instrument pipelines with observability tooling to surface issues proactively. Build and maintain reusable data assets - curated datasets, aggregations, and data marts - that power FinOps dashboards, showback/chargeback reporting, and AI/ML model inputs. Microservices & Platform Feature Development Design and build modular microservices and APIs that expose FinOps data and AI/ML capabilities as reusable services - enabling other teams and internal platforms to consume cost intelligence programmatically. Contribute new features and capabilities to CBRE's FinOps platform, translating analyst and engineering requirements into well-structured, production-ready service components. Integrate data and AI services with internal platforms such as AIDP (Automated Infrastructure Deployment Platform) and ECMP (Enterprise Container Management Platform), embedding cost signals directly into existing engineering workflows. Follow software engineering best practices in all platform work: clean interfaces, unit testing, API versioning, containerization, and CI/CD deployment pipelines. Identify opportunities to refactor or modularize existing FinOps platform components, improving reliability, scalability, and ease of maintenance. AI/ML Model Development Develop, train, and evaluate machine learning models that address core FinOps use cases: cost anomaly detection, spend forecasting, workload rightsizing recommendations, and commitment coverage optimization. Implement and maintain MLOps pipelines for model versioning, automated retraining, performance monitoring, and drift detection as cloud usage patterns evolve. Experiment with and apply generative AI and LLM capabilities to FinOps workflows - such as natural language interfaces for cost querying, AI-assisted tagging remediation, or intelligent cost allocation suggestions. Collaborate with FinOps analysts to validate model outputs against business expectations, refining approaches iteratively based on real-world feedback. Document model design, feature engineering decisions, and evaluation results to ensure team-wide understanding and reproducibility. Quality, Governance & Best Practices Implement and uphold data governance standards: lineage tracking, cataloging, column-level security, and RBAC for sensitive cost and financial data. Follow and actively contribute to team engineering standards for code quality, testing, documentation, and CI/CD - raising the bar for the team's collective output through code reviews and knowledge sharing. Apply extensive and diversified knowledge of data engineering principles, advanced techniques, and theories to solve complex FinOps data challenges. Lead by example and model behaviors consistent with CBRE RISE values - Respect, Integrity, Service, and Excellence. What You'll Need Bachelor's Degree preferred relevant experience. In lieu of a degree, a combination of experience and education will be considered. Hands on data engineering experience in production environments: ELT/ETL pipelines, lakehouses, data warehouses, and large scale analytical workloads. Applied AI/ML engineering experience with a track record of delivering models into production. Software or platform engineering experience building APIs, microservices, or backend services in a cloud environment. Experience working with cloud billing data, FinOps tooling, or cloud cost management across at least one major cloud provider (Azure, AWS, or GCP). Technical Skills Data engineering: strong proficiency with Apache Spark, dbt, Airflow / Azure Data Factory or equivalent orchestration, Data Lake / Apache Iceberg, and advanced SQL for large scale transformations. Microservices & APIs: experience building modular, production grade microservices and REST APIs using Python (FastAPI, Flask, or equivalent); understanding of service design principles including versioning, fault tolerance, and observability. Cloud platforms: working knowledge of cost and usage data schemas across Azure (Cost Management, Synapse / Fabric), AWS (Cost Explorer, Athena, Glue), and GCP (BigQuery). AI/ML: proficiency in the Python ML ecosystem (scikit learn, XGBoost, Prophet), experience building and deploying models, and familiarity with MLflow or equivalent MLOps tooling. Generative AI: practical experience with LLM APIs (Azure OpenAI, OpenAI, or equivalent) for building intelligent interfaces or automating classification, tagging, and cost attribution tasks. Software engineering fundamentals: Python, SQL, Git, CI/CD, containerization (Docker / Kubernetes), and infrastructure as code familiarity (Terraform or Bicep). Data governance: experience with data cataloging, lineage, access controls, and data quality frameworks in production data platforms. Competencies In-depth expertise in leading edge data engineering and AI/ML techniques and technologies, with the ability to identify and solve complex technical problems independently. Multi dimensional, conceptual, and innovative thinking - able to design creative solutions to FinOps data and AI challenges without direct supervision. Strong collaboration and communication skills - comfortable working alongside engineers and analysts at all levels, explaining technical work clearly to non technical stakeholders. Detail oriented approach to data quality, model reliability, and engineering rigor - takes ownership of the accuracy and dependability of the work delivered. Expert organizational skills with an unrivaled inquisitive mindset. In depth knowledge of Microsoft Office products including Word, Excel, and Outlook. Preferred Qualifications FinOps Certified Practitioner (FOCP) designation from the FinOps Foundation. Cloud data or AI certifications (e.g., Azure Data Engineer Associate, AWS Data Engineer Associate, GCP Professional Data Engineer, or equivalent ML/AI certifications). Experience with real time or near real time cost event streaming and alerting pipelines. Familiarity with FinOps Foundation framework concepts including the Inform, Optimize, and Operate phases. Why CBRE When you join CBRE, you become part of the global leader in commercial real estate services and investment that helps businesses and people thrive. We are dynamic problem solvers and forward thinking professionals who create significant impact. Our collaborative culture is built on our shared values - respect, integrity, service and excellence - and we value the diverse perspectives, backgrounds and skillsets of our people. At CBRE, you have the opportunity to realize your full potential. Our Values in Hiring At CBRE, we are committed to fostering a culture where everyone feels they belong. We value diverse perspectives and experiences, and we welcome all applications. Applicant AI Use Disclosure We value human interaction to understand each candidate's unique experience, skills and aspirations. We do not use artificial intelligence (AI) tools to make hiring decisions, and we ask that candidates disclose any use of AI in the application and interview process.
This is an exciting opportunity where we're exploring local Sr Lead Security Engineering talent who need flexibility, with the potential to present qualified candidates to managers for part-time or job-share roles. Part-time hours where two people share the responsibilities. We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Site Reliability Engineer at JPMorgan Chase within the Commercial & Investment Bank - Markets - Commodities Technology, you hold a technical leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issues facing them. You will will set the vision, strategy, and operating model for our SRE transformation - enabling our business-aligned support teams to deliver higher reliability, stronger resilience, and a measurably better end-user experience across the board. Job responsibilities Defines the SRE vision, north-star outcomes, and multi-year roadmap for the Production Management team, aligned to both CIB and JPM Global Technology priorities. Establishes the SRE operating model across global regions (ways of working, intake, prioritization, engagement with engineering teams and production support). Partners with business-aligned Production Support leads to embed SRE practices consistently and act as a force multiplier - coaching them on reliability thinking, prioritization, and "engineering out" operational load. Builds and develops a small, high-impact core SRE team (and/or virtual SRE community of practice) that scales reliability improvements across many application flows. Defines and implements standards for: Service cataloging, SLO/SLI frameworks and error budgets, incident response maturity, blameless post-incident reviews, resiliency patterns, capacity, performance, and scalability engineering. Drives service reviews with evidence-based reporting (availability, latency, incident trends, MTTR/MTTD, change failure rate, customer impact). Champions AI adoption and deliver AI-enabled capabilities to reduce operational toil and improve speed/quality of response. Sets direction for observability across logs/metrics/traces, including instrumentation standards, golden signals, and end-user journey monitoring. Improves alert quality and routing: reduce false positives, improve actionable alerts, and tighten feedback loops to engineering teams. Builds strong partnerships with application development teams, platform/infrastructure partners, and governance functions. Communicates clearly and credibly at all levels-from engineers to senior technology and business stakeholders. Uses enterprise-authorized AI capabilities within the work environment to accelerate major-incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements. Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., CI/CD quality checks, test/validation automation, and operational readiness), ensuring traceability/auditability, resiliency, and security controls. Required qualifications, capabilities, and skills 10+ years of experience in technology support, production/application support, DevOps, or infrastructure management. Demonstrated experience leading SRE/reliability engineering or production engineering transformations in a complex enterprise environment. Strong engineering background: ability to design, build, and deliver automation and reliability solutions. Fluency & expertise in Python Deep practical knowledge of: SLOs/SLIs, error budgets, incident management, postmortems, observability design across metrics/logs/traces and distributed systems troubleshooting, resilience engineering, performance/capacity management, and change risk reduction. Proficiency and experience with telemetry (logs/metrics/traces) collection using tools and standards such as Prometheus, Open Telemetry, Datadog, Dynatrace, Splunk. Experience delivering automation at scale (scripting, workflow automation, runbook automation, CI/CD-integrated guardrails). Proven leadership skills: influencing without authority, coaching leaders, and building communities of practice. Strong judgment around risk, security, and controls-especially when applying AI to production workflows. Demonstrated experience using enterprise-authorized AI capabilities within the work environment to improve SRE workflows (e.g., incident investigation support and knowledge capture) with strong validation habits and awareness of data sensitivity. Ability to evaluate AI-assisted operational recommendations for correctness and risk, define appropriate guardrails for team usage, and ensure outcomes align to resiliency and security expectations. Preferred qualifications, capabilities, and skills Familiarity with Athena / prior experience in Athena Your Pathway to Jobsharing at JPMorgan - IT'S A 2 BRAINER! At JPMorgan we are passionate about supporting different ways of working to support our talent in the flexibility they need. We know that Jobshare is a fantastic way to hire talent for the firm that offers both the flexibility that you need whilst providing the consistency that our business requires. Jobshare is 2 people working part time hours with full time powers. This role is part of a Jobshare opportunity and therefore a part-time role at 19hours
Sep 23, 2026
Full time
This is an exciting opportunity where we're exploring local Sr Lead Security Engineering talent who need flexibility, with the potential to present qualified candidates to managers for part-time or job-share roles. Part-time hours where two people share the responsibilities. We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Site Reliability Engineer at JPMorgan Chase within the Commercial & Investment Bank - Markets - Commodities Technology, you hold a technical leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issues facing them. You will will set the vision, strategy, and operating model for our SRE transformation - enabling our business-aligned support teams to deliver higher reliability, stronger resilience, and a measurably better end-user experience across the board. Job responsibilities Defines the SRE vision, north-star outcomes, and multi-year roadmap for the Production Management team, aligned to both CIB and JPM Global Technology priorities. Establishes the SRE operating model across global regions (ways of working, intake, prioritization, engagement with engineering teams and production support). Partners with business-aligned Production Support leads to embed SRE practices consistently and act as a force multiplier - coaching them on reliability thinking, prioritization, and "engineering out" operational load. Builds and develops a small, high-impact core SRE team (and/or virtual SRE community of practice) that scales reliability improvements across many application flows. Defines and implements standards for: Service cataloging, SLO/SLI frameworks and error budgets, incident response maturity, blameless post-incident reviews, resiliency patterns, capacity, performance, and scalability engineering. Drives service reviews with evidence-based reporting (availability, latency, incident trends, MTTR/MTTD, change failure rate, customer impact). Champions AI adoption and deliver AI-enabled capabilities to reduce operational toil and improve speed/quality of response. Sets direction for observability across logs/metrics/traces, including instrumentation standards, golden signals, and end-user journey monitoring. Improves alert quality and routing: reduce false positives, improve actionable alerts, and tighten feedback loops to engineering teams. Builds strong partnerships with application development teams, platform/infrastructure partners, and governance functions. Communicates clearly and credibly at all levels-from engineers to senior technology and business stakeholders. Uses enterprise-authorized AI capabilities within the work environment to accelerate major-incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements. Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., CI/CD quality checks, test/validation automation, and operational readiness), ensuring traceability/auditability, resiliency, and security controls. Required qualifications, capabilities, and skills 10+ years of experience in technology support, production/application support, DevOps, or infrastructure management. Demonstrated experience leading SRE/reliability engineering or production engineering transformations in a complex enterprise environment. Strong engineering background: ability to design, build, and deliver automation and reliability solutions. Fluency & expertise in Python Deep practical knowledge of: SLOs/SLIs, error budgets, incident management, postmortems, observability design across metrics/logs/traces and distributed systems troubleshooting, resilience engineering, performance/capacity management, and change risk reduction. Proficiency and experience with telemetry (logs/metrics/traces) collection using tools and standards such as Prometheus, Open Telemetry, Datadog, Dynatrace, Splunk. Experience delivering automation at scale (scripting, workflow automation, runbook automation, CI/CD-integrated guardrails). Proven leadership skills: influencing without authority, coaching leaders, and building communities of practice. Strong judgment around risk, security, and controls-especially when applying AI to production workflows. Demonstrated experience using enterprise-authorized AI capabilities within the work environment to improve SRE workflows (e.g., incident investigation support and knowledge capture) with strong validation habits and awareness of data sensitivity. Ability to evaluate AI-assisted operational recommendations for correctness and risk, define appropriate guardrails for team usage, and ensure outcomes align to resiliency and security expectations. Preferred qualifications, capabilities, and skills Familiarity with Athena / prior experience in Athena Your Pathway to Jobsharing at JPMorgan - IT'S A 2 BRAINER! At JPMorgan we are passionate about supporting different ways of working to support our talent in the flexibility they need. We know that Jobshare is a fantastic way to hire talent for the firm that offers both the flexibility that you need whilst providing the consistency that our business requires. Jobshare is 2 people working part time hours with full time powers. This role is part of a Jobshare opportunity and therefore a part-time role at 19hours
