GCP FinOps Engineer

- Optimise large-scale data analytics workloads via partitioning, clustering, query rewrites, storage format improvements, and lifecycle policies. - Tune containerised microservices by recalibrating CPU/memory requests, improving autoscaling efficiency, and restructuring workload placement on cost-efficient compute. - Redesign workflow orchestration pipelines for parallel execution, higher concurrency, and offloading heavy tasks to lower-cost execution environments. - Analyse distributed data-processing pipelines to right-size worker types, adjust scaling thresholds, and adopt low-cost compute for batch workloads. - Reduce log-processing and storage overhead through log-level standardisation, routing rules, exclusion filters, and retention optimisation. - Implement storage-tiering strategies based on access patterns and enforce lifecycle rules to minimise cold data retention costs. - Improve relational database performance through index tuning, connection optimisation, and instance right-sizing. - Enhance horizontally scalable database performance via autoscaling policies, index improvements, and mitigation of read/write hotspots. - Build dashboards, budgets, alerts, and guardrails to drive ongoing cost governance and financial accountability. - Collaborate with engineering teams to embed cost-efficient architecture patterns and operational best practices.