GCP Finops Engineer

Job Title: GCP FinOps Engineer

Location: Newport, UK (Hybrid)

Contract Duration: 6 Months

IR35 Status: Inside IR35

Role Overview

We are seeking an experienced GCP FinOps Engineer to optimise cloud spend, performance, and operational efficiency across large-scale data analytics and containerised workloads. You will work closely with engineering, data, and product teams to embed cost-efficient architectures, enforce financial governance, and implement best practices across Google Cloud environments.

Responsibilities

• 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.

Key Skills / Knowledge

• 5+ years of hands-on experience in Google Cloud.

• Strong understanding of GCP data services, including indexing, slots, pruning, partitioning, and clustering.

• Expert-level Kubernetes & GKE resource tuning.

• Hands-on experience with Dataflow pipelines and worker optimisation.

• Strong Airflow/Composer knowledge (DAG design, scheduling, PodOperator).

• Deep understanding of Cloud Logging, routing, sinks, and exclusion filters.

• Experience with Cloud Spanner autoscaling, indexing, and schema optimisation.

• Cloud SQL performance tuning and indexing.

• Ability to analyse billing data, resource consumption, and quantify cost savings.

• Experience using GCP Cost Explorer, Recommender API, and Billing Export.

• Build dashboards, alerts, and budget guardrails for cost governance.

• Excellent communication and stakeholder management skills.

• Ability to collaborate across engineering, data, and product teams.

• Structured problem-solving mindset; ownership-driven, proactive, and independent.