FinOps Data Engineer

About the Role

We're looking for a FinOps Data Engineer to help drive cost optimization and cloud financial governance across our AWS infrastructure and GCP footprint. You'll work cross-functionally with Engineering, Finance, and Product to find and ship savings, build the tooling necessary to maximize efficiency of every dollar spent in the cloud, and cultivate a culture of cost awareness across engineering teams. This role owns the data side of that mission: the models, pipelines, and analysis that make cost, usage, and margin numbers trustworthy across the company.

This is a hands-on engineering role, not an analyst seat. You'll do the schlep work other teams don't have time for, digging into a bucket's access pattern, writing the dbt model or Python pipeline that ships the fix, building the cost-vs-revenue mart that answers a margin question once instead of every time, and chasing a number until it reconciles. Strong communication matters as much as technical depth, you'll regularly explain a savings case or a data model's implications to an engineering team, a finance stakeholder, and a leadership audience in the same week.

What You'll Be Responsible For

In this role, you'll:

• Find and ship savings across AWS and GCP: Analyze billing, usage, and cost trends to identify opportunities, rightsizing, storage lifecycle policies, data transfer, and spot/auto-scaling. Take ideas from identification through to a realized dollar figure, not just a recommendation.

• Build and maintain the FinOps data pipeline: Design and run the BigQuery/dbt models that turn raw billing and usage data (CUR, Storage Lens) into reconciled, trustworthy mart tables, the foundation everything else in FinOps is built on.

• Build cost-vs-revenue and customer segmentation models: Model unit economics and margin by product line and customer segment, and extend coverage as new products and segments come online.

• Apply data science to cost and usage data: Build forecasting models, detect anomalies statistically rather than by threshold alone, and quantify savings opportunities (e.g. storage lifecycle, request-cost patterns) with rigor a spreadsheet can't match.

• Build and maintain cost attribution: Tagging policies, cost allocation, and chargeback/showback so engineering teams can see and own their own spend. This is foundational work other savings work depends on.

• Automate cost governance: Build automated cost reporting, anomaly detection, and alerting. Write the scripts and infrastructure-as-code (Python, Terraform, Lambda) that enforce cost policy rather than relying on manual review.

• Partner with engineering on architecture decisions: Bring cost into the room early, cost-efficiencies are cheaper to get right at design than to retrofit.

• Work with Finance on forecasting and reporting: Support budget tracking and financial KPIs; build dashboards and self-serve analytics engineering and finance can both read.

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