Senior Data Engineer

About this role

As a Senior Data Engineer, you will own the end-to-end engineering lifecycle for key components and services—designing, coding, testing, deploying, and operating solutions that are secure, reliable, and maintainable. AI-native development: Use AI tools across the full SDLC in a disciplined way. Own the quality of AI-generated requirements, designs, and code — yours and your teammates' — and apply Responsible AI practices

Data for AI: Build the training, feature, retrieval, grounding, and evaluation datasets that LLMs, and agents depend on. Partner with PM and engineering to make data contracts, freshness, drift signals, and offline/online consistency first-class concerns. Design and architecture: Lead design discussions for your project area, evaluate tradeoffs across batch vs

streaming, warehouse vs. lakehouse, ELT vs. ETL, and storage choices for analytical, feature, and vector workloads

Own architectural decisions with minimal oversight. Customer requirements: Partner with PM and engineering to define data requirements; ensure feedback loops on data quality, usage, model performance, and downstream product impact are in place. High-quality code: Write extensible, secure, performant code for pipelines, transformations, and supporting services

Apply modern patterns including GenAI-assisted development. Drive code reviews and best practices at the product level. Testing and quality: Own the test strategy for your area, including data contract tests, schema validation, freshness checks, distribution and drift monitoring, and offline/online parity

Improve the test suite and use AI tools for test automation. Dependencies and coordination: Identify cross-team data dependencies, manage upstream producer and downstream consumer impact, and resolve conflicts when semantics or schemas change in ways that affect models or downstream products. Planning: Drive your workgroup's project and release plans

Break work into a roadmap including backfills, migrations, and model-

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