CBRE UK
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.
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.