Software Engineer Iii, Cloud Bigtable Sql and Analytics

Google Google · Big Tech · New York, NY +1

Software Engineer to help build the next generation of Google Cloud's Feature Store, transforming it into an Agentic Data Cloud Feature Store. This role involves designing and implementing core "SQL for ML" capabilities like continuous materialized views, real-time counters, and vector search (ANN) to enable modern agentic application development. The position requires building high-performance, scalable, and reliable distributed systems and includes participation in a production on-call rotation.

What you'd actually do

  1. Write product or system development code.
  2. Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
  3. Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
  4. Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  5. Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.

Skills

Required

  • software development
  • large-scale infrastructure
  • distributed systems
  • compute technologies
  • storage
  • C++
  • Java
  • distributed systems concepts (concurrency, consistency models, sharding)

Nice to have

  • Master's degree or PhD in Computer Science or related technical fields
  • designing public-facing APIs or libraries (CLI, REST, gRPC) with a focus on backward compatibility and usability
  • how databases work under the hood (Query Processing, Query Optimization, Storage Engines)
  • SQL parsers
  • NoSQL paradigms (Cassandra, HBase, DynamoDB)
  • data processing frameworks (Spark, Beam)

What the JD emphasized

  • agentic application development

Other signals

  • building the next generation of our Feature Store
  • intersection of database internals and ML infrastructure
  • designing and implementing core "SQL for ML" capabilities
  • continuous materialized views
  • real-time counters
  • vector search (ANN)
  • enabling customers to move from traditional ML to modern agentic application development
  • building high-performance, scalable, and reliable distributed systems