The opportunity
Supabase was born-remote and open-source-first. We believe our globally distributed team is our secret weapon in building tools developers love.
What you'll do
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.
What they're looking for
- 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.
- 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).