Software Engineer 5 - Platform Data Products

Role overview A senior full-stack engineering position on an internal platform team focused on understanding and optimizing large-scale cloud and AI infrastructure. The work blends product engineering, analytics, and data-rich application development, with the engineer serving as the first dedicated software engineer on a team otherwise composed of analytics engineers, data engineers, and data scientists. The role emphasizes autonomy, direct ownership, and the ability to set technical direction without a large engineering organization behind it.

Responsibilities - Design and ship full-stack features for infrastructure cost and usage tooling, from React and TypeScript frontends through GraphQL backend services - Help build and launch a product for analyzing AI productivity, cost, and usage, including architecture, data model design, and tooling - Contribute data produced by the team to other internal surfaces such as developer platform and ML/AI platform tooling - Build reusable components and shared infrastructure for visualization and analytics engineers - Partner with analytics and data engineers on the data models behind these products without owning pipeline construction

Requirements - Track record of designing, building, and shipping full-stack applications - Experience contributing to internal systems you do not own, with the ability to onboard to new stacks autonomously and exercise strong design judgment - Strong TypeScript and React skills, plus backend experience in Node.js, GraphQL, or similar technologies sufficient to own a data feature end to end - Data fluency including SQL and comfort reasoning about data models in messy, changing environments - Demonstrated ability to set direction and see work through independently, with strong proactive collaboration and communication

Nice to have - GraphQL API design experience or familiarity with analytics data stores such as Druid or Iceberg - Interest in survey design or product analytics instrumentation - Prior collaboration with data scientists, analytics engineers, or data engineers on small teams

Benefits and work setup The team operates with a high-autonomy culture that emphasizes accountability over process, keeping approvals and management layers light. Engineers are trusted to prioritize their own work and own outcomes rather than executing a backlog assigned by others. The environment values diversity of background and building durable products over one-off analyses.