Principal Cloud Engineer; Terraform

Principal Cloud Engineer (Terraform)

London

As a CBRE Systems Engineer Principal
-AI & CLoud Engineer, you will be an embedded technical expert within the Cloud Engineering & Fin Ops team, working alongside fellow Fin Ops 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 Fin Ops 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 Fin Ops analysts, cloud engineers, and platform leads to deliver engineering work that raises the quality and capability of the entire practice.

• Build, maintain, and improve ELT/ETL pipelines that ingest billing, usage, and tagging data from AWS, Azure, and GCP into CBRE's centralized Fin Ops 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 Fin Ops 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 Fin Ops 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 Fin Ops dashboards, showback/chargeback reporting, and AI/ML model inputs.

• Design and build modular microservices and APIs that expose Fin Ops 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 Fin Ops 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 Fin Ops platform components, improving reliability, scalability, and ease of maintenance.

• Develop, train, and evaluate machine learning models that address core Fin Ops 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 Fin Ops workflows - such as natural language interfaces for cost querying, AI-assisted tagging remediation, or intelligent cost allocation suggestions.
• Collaborate with Fin Ops 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.

• Implement and uphold data governance standards: lineage tracking, cataloging, column-level security, and RBAC for sensitive cost and…