FinOps Engineer

About the position

As a FinOps Engineer within the Advanced Analytics Team, you will own the financial operations discipline for the engineering platform. You will make cloud and AI spend transparent, attributable, and predictable across production workloads spanning AI services, APIs, and frontend applications.

Your work sits at the intersection of engineering and finance: you build the cost visibility, forecasting, and optimization practices that let delivery teams move fast while keeping unit economics under control. You will work closely with Platform Engineers, SREs, AI Architects, and Engineering Managers to embed cost awareness into architecture and operations, and you will partner with Finance and Procurement to align cloud consumption with budgets, commitments, and chargeback models.

A significant and growing part of the role is governing the cost of AI workloads, where model inference, token consumption, and GPU capacity are primary cost drivers.

Key responsibilities

• Build and maintain cost visibility across the platform: implement cost attribution and tagging standards, per-team and per-workload chargeback/showback, and dashboards that make spend legible to both engineers and Finance.
• Own cloud cost forecasting and budgeting: model consumption trends, produce variance analysis against budget, and surface anomalies before they become overruns.
• Govern AI/LLM spend: attribute inference cost per team, workload, and model tier; track token consumption and GPU/capacity utilization; recommend model-tier and routing choices that meet quality bars at lower unit cost.
• Drive resource right-sizing programs: identify idle and over-provisioned resources across compute, databases, and storage; partner with SREs and Platform Engineers to reclaim waste without harming reliability.
• Manage commitment-based discounts: plan and track reserved instances, savings plans, and capacity reservations across Azure services; balance commitment coverage against flexibility.
• Establish FinOps practices and guardrails: budget alerts, cost policies (Azure Policy, budgets), and cost-aware review gates integrated into the platform's delivery workflow.
• Instrument cost telemetry: integrate Azure Cost Management, tagging, and usage data into the observability stack so cost sits alongside reliability metrics.
• Produce regular cost reporting for senior leadership and Finance: unit economics, cost-per-workload trends, savings realized, and optimization roadmap.
• Embed cost awareness into architecture and design reviews: advise Backend Engineers, ML Engineers, and AI Architects on cost-efficient patterns and trade-offs.
• Collaborate with Finance and Procurement on cloud commitments, vendor negotiations, and alignment of consumption with the financial planning cycle.

Required Experience & Skills

• 5+ years of professional experience in FinOps, cloud cost engineering, platform engineering, or a related DevOps/finance-adjacent role.
• Strong hands-on experience with Azure cost management: Azure Cost Management + Billing, budgets, tagging strategy, and cost allocation; equivalent AWS/GCP cost tooling experience is transferable.
• Solid understanding of cloud pricing and commitment models: reserved instances, savings plans, capacity reservations, and consumption-based pricing.
• Working knowledge of Kubernetes cost drivers: AKS node pools, autoscaling, resource requests/limits, and multi-tenant cost attribution across namespaces.
• Data proficiency for cost analysis: SQL and Python (pandas) for querying, modeling, and reporting cost and usage data; comfort building dashboards (Grafana, Power BI, or similar).
• Understanding of AI/LLM cost economics: inference pricing, token accounting, model-tier trade-offs, and GPU/capacity utilization.
• Experience defining and driving cost-optimization programs with measurable savings and clear ownership.
• Ability to translate between engineering and finance.

Ways of Working

• Comfortable in agile, iterative delivery environments with personal ownership and accountability for platform unit economics.
• Clear communicator across global, cross-functional stakeholders; able to translate cloud consumption into business and financial impact for non-technical audiences.
• Pragmatic and collaborative: you optimize cost without becoming a bottleneck for delivery teams, and you make the efficient path the easy path.
• Proactive learner with pragmatic adoption of AI-assisted developer tools (e.g., GitHub Copilot, Claude Code) to automate cost reporting and analysis.

Nice to Have

• FinOps Foundation certification (FinOps Certified Practitioner) or equivalent.
• Experience governing AI/ML workload cost at scale: inference platforms, GPU scheduling economics, or model-serving cost attribution.
• Familiarity with Infrastructure as Code (Terraform, Bicep) to codify cost guardrails and tagging policy.
• Experience with Databricks or similar data platform cost management and workload-level attribution.
• Exposure to unit-economics modeling for product or per-customer profitability.
• Experience in regulated industries (insurance, finance, healthcare) where cost attribution, auditability, and financial governance are standard practice.

What we offer

• Contract under Polish law: B2B or Umowa o Pracę.
• Benefits such as private medical care, group insurance, and Multisport card.
• English classes.
• Hybrid work: at least 1 day per week on-site in Warsaw (Mokotów).
• Opportunity to work with experienced professionals.
• High standards of work and focus on code quality.
• New technologies in use.
• Continuous learning and growth.
• International team.
• Pinball, PlayStation, and more on-site.