Staff Engineer, Finops

Amplitude Amplitude · Data AI · San Francisco, CA · Engineering : Infrastructure

Staff FinOps Engineer to own the technical backbone for Amplitude's cost visibility across cloud, observability, AI/ML, and SaaS vendors. This role involves designing and building systems, implementing optimizations, and leading cross-functional initiatives to ensure efficient and transparent cloud and vendor spend.

What you'd actually do

  1. Own our cost & vendor data platform — design, build, and maintain the pipelines, models, and storage that consolidate cost and usage data across AWS, GCP, Kubernetes, observability tooling, AI providers, and other strategic vendors into a single, trusted source of truth
  2. Turn data into decisions and savings — partner with Engineering, Infrastructure, Product, and Finance to translate raw cost and usage signals into clear insights, recommendations, and playbooks that influence roadmaps and architecture
  3. Personally implement cost optimizations — dive into services, workloads, and vendor configurations to execute optimizations yourself when needed (e.g., rightsizing, storage tiering, query optimization, metrics/logs reduction, plan changes), not just make recommendations
  4. Build self-service FinOps tooling — develop dashboards, scorecards, alerts, bots, and internal tools that make it easy for service owners to understand their spend, identify waste, and track progress against optimization goals
  5. Establish guardrails and best practices — define and codify standards for cost-efficient architecture (e.g., instance families, storage patterns, data retention, observability practices, AI usage patterns) and embed them into infrastructure-as-code, CI/CD, and review processes

Skills

Required

  • 7+ years of hands-on experience in FinOps, SRE, Platform, or Infrastructure engineering
  • Track record of reducing spend and improving unit economics in a modern cloud environment (AWS preferred)
  • Production-quality code (Python, TypeScript, or similar)
  • Build data pipelines and dashboards
  • Financial concepts (budgets, forecasts, ROI, unit economics)
  • Worked with cost & usage data (e.g., AWS CUR, SaaS vendor exports, observability billing data)
  • Model cost data into schemas and metrics
  • Shipped real optimizations yourself (changed infrastructure, configurations, or code to reduce spend)

Nice to have

  • Partner directly with Engineering, Finance, and Product
  • Thrives in ambiguity
  • Learns new domains quickly
  • Cares as much about business outcomes as technical elegance
  • Partner closely with FP&A and vendor owners
  • Run reviews, office hours, and targeted trainings
  • Define KPIs and build telemetry
  • Subject-matter expert on cloud pricing changes, vendor offerings, and FinOps best practices
  • Pragmatic, opinionated guidance

What the JD emphasized

  • Own our cost & vendor data platform
  • AI providers
  • AI usage patterns
  • cost guardrails
  • AI vendor consolidation