Remote, Global · Remote
Full-time
Mid level
1 week ago
Verified apply link — checked 4 hours ago
Salary undisclosed
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. You Might Be a Good Fit If You - Have strong AWS expertise and working GCP knowledge, including pricing models, billing structures, and cost optimization mechanics. - Have data engineering depth: dbt, BigQuery (or an equivalent modern warehouse), SQL at scale, and data modeling for analytics (dimensional modeling, incremental models, testing). - Have data science fundamentals: statistical forecasting, anomaly detection, and experimentation, and know when a rigorous model beats a rule of thumb. - Have automation and scripting skills: Python, Terraform/ pulumi, Lambda, or Bash. - Are comfortable with cost/billing tooling: AWS Cost Explorer, Cost & Usage Reports, GCP Billing Reports, and BI/dashboarding (we use Hex). - Have experience with, or are comfortable picking up, Linear and Notion as the system of record for tracking and documenting FinOps work. - Experience with a dedicated cloud cost management platform (e.g. CloudZero, Vantage, or similar) is a plus, not required. - Can analyze cost data and present it clearly to both technical and non-t
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