Principal Cloud Engineer (Terraform), London

About the role

Cloud engineering and data pipelines

• Ingest billing, usage, and tagging data from AWS, Azure, and GCP into a centralized FinOps data store.
• Build layered data models and transformations using patterns and tools such as dbt, Spark, and cloud-native processing services.
• Manage production pipelines, including orchestration, scheduling, dependencies, error handling, alerting, and incident response.
• Apply data quality, validation, and observability practices; maintain curated datasets, aggregations, and data marts.

Platform and AI/ML development

• Build microservices and APIs that expose FinOps data and AI/ML capabilities; contribute production-ready platform features.
• Integrate data and AI services with AIDP and ECMP, and follow software engineering practices including testing, API versioning, containerization, and CI/CD.
• Develop models for cost anomaly detection, spend forecasting, workload rightsizing, and commitment coverage optimization.
• Maintain MLOps practices for model versioning, retraining, monitoring, and drift detection; explore generative AI and LLM applications in FinOps.

Experience and expertise

• Deep, hands-on expertise in data engineering and AI/ML.
• Extensive knowledge of data engineering principles and advanced techniques for solving complex FinOps data challenges.
• Work as a principal-level individual contributor with cross-functional collaborators.

Quality and governance

• Implement data governance, lineage tracking, cataloging, column-level security, and role-based access controls for sensitive cost and financial data.
• Contribute to engineering standards for code quality, testing, documentation, and CI/CD through code reviews and knowledge sharing.
• Document model design, feature engineering decisions, and evaluation results.