Software Engineer 5 - Platform Data Products

Netflix is a company focused on pushing the boundaries of storytelling and technology. They are seeking a Senior Software Engineer to join their Platform Data Science & Engineering team, where the main responsibilities include building full stack applications and improving the efficiency of their Cloud and AI infrastructure.

Responsibilities:

• Build and ship full stack features for our infrastructure cost and usage tooling, from the React and TypeScript frontend through the GraphQL services behind it
• Help build and launch our product for analyzing AI productivity, cost and usage including designing the architecture, help shaping the data model, and build the tooling
• Contribute into other internal tooling surfaces (e.g. developer platform, ML/AI platform tooling) by contributing pertinent data produced from our team
• Build reusable components and shared infrastructure to enable our visualization and analytics engineers
• Partner with analytics engineers and data engineers on the data models behind these products. You won't build the pipelines, but you need to reason about them

Requirements:

• You've designed, built, and shipped full stack applications
• Experience contributing to internal systems and tools you don't own, including autonomously onboarding to new application stacks, strong design and architecture judgment, and proactive collaboration and communication
• Strong TypeScript and React, plus enough backend (Node.js, GraphQL, or similar) to own a data feature end to end
• Enough data fluency (SQL, and comfort reasoning about a data model) that a data-heavy product isn't a cold start. You don't need to be a data engineer, but you do need to be at home in messy, changing data
• A track record of owning work on your own. You'd be our first software engineer, so you'll set direction and see it through without a big engineering org behind you
• GraphQL API design, or work with Druid, Iceberg, or similar analytics data stores
• An interest in survey design or product analytics instrumentation, which is where AIPI may go next
• You've worked closely with data scientists, analytics engineers, or data engineers on a small team