Staff Engineer, FinOps

About The Role & Team - San Francisco, CA 

Amplitude is the leading AI analytics platform, and our ability to invest wisely in infrastructure and SaaS is a key part of how we keep that lead. The FinOps Engineering function sits at the intersection of engineering, finance, and product — owning the data, tooling, and practices that make our cloud and vendor spend both efficient and transparent.

As a Staff FinOps Engineer, you will own the technical backbone that powers Amplitude's cost visibility across major vendors (cloud hosting, observability, AI/ML, and core SaaS), and partner directly with Engineering, Finance, and Product to turn that visibility into real savings and better decisions. This role is both hands-on engineer and program driver: you'll design and build systems, roll up your sleeves to implement optimizations, and lead cross-functional initiatives, reviews, and long-range planning.

You'll be a small, high-impact team of one with a large surface area, partnering closely with leaders across Engineering, Platform, Finance, and Product — from building cost data pipelines and reporting, to defining cost guardrails for new architectures, to helping forecast the impact of product and infrastructure bets, and handling all Engineering vendor negotiations, licensing, and contracts. The right person thrives in ambiguity, learns new domains quickly, and cares as much about business outcomes as technical elegance.

As a Staff FinOps Engineer, you will:

• 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
• 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
• 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
• 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
• 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
• Partner closely with Finance on forecasting — collaborate with FP&A and vendor owners to forecast short- and long-term spend, model the impact of new features and infrastructure changes, and reconcile actuals against forecasts
• Lead cross-functional cost programs — drive multi-quarter initiatives (e.g., Datadog/observability optimization, storage lifecycle redesign, AI vendor consolidation) from problem definition through design, execution, and ongoing measurement
• Raise the FinOps bar across Amplitude — run reviews, office hours, and targeted trainings to help teams interpret cost data, reason about trade-offs, and incorporate cost as a first-class engineering and product constraint
• Instrument and measure everything — define KPIs and build telemetry around cost programs (e.g., $ saved, unit economics, ROI, impact on performance/reliability) to ensure we're shipping changes that actually move the business
• Act as a subject-matter expert — stay current on cloud pricing changes, vendor offerings, and FinOps best practices, and bring pragmatic, opinionated guidance back into our architecture, contracts, and tooling

You'll be a great addition to the team if you:

• Have 7+ years of hands-on experience in FinOps, SRE, Platform, or Infrastructure engineering, with a track record of reducing spend and improving unit economics in a modern cloud environment (AWS preferred)
• Are as comfortable in code as in spreadsheets — you can write production-quality code (Python, TypeScript, or similar), build data pipelines and dashboards, and you're fluent enough with financial concepts (budgets, forecasts, ROI, unit economics) to partner credibly with Finance
• Know how to build cost data systems — you've worked with cost & usage data (e.g., AWS CUR, SaaS vendor exports, observability billing data) and can model it into schemas and metrics that make sense to engineers, leadership, and Finance
• Have shipped real optimizations yourself — you can point to concrete examples where you personally changed infrastructure, configurations, or code to reduce spend (not just recommended changes for others to implement)
• Operate with high ownership and autonomy — you like owning ambiguous, cross-functional problems end-to-end, from framing and alignment through technical design, implementation, rollout, and ongoing iteration
• Balance program management with engineering depth — you're comfortable driving a roadmap, aligning stakeholders, and tracking outcomes, while also diving into details like Terraform modules, Kubernetes manifests, or Datadog/SaaS billing settings
• Communicate clearly with diverse audiences — you can explain complex technical and financial trade-offs to engineers, PMs, Finance partners, and executives in a way that is concise, data-driven, and actionable
• Have strong data and analytics instincts — you're comfortable working with SQL and analytics tools, and you default to instrumenting your work and validating that changes improved cost, performance, or reliability
• Care about reliability and developer experience — you optimize cost without sacrificing performance, availability, or developer velocity, and you're thoughtful about where to introduce friction vs. where to automate
• Are curious and fast-learning — you enjoy picking up new domains (e.g., AI infra, new observability vendors, pricing models) and applying that knowledge quickly to make better decisions