Engineering Manager, Spanner Search

Google Google · Big Tech · Kirkland, WA +1

This role is for an Engineering Manager at Google Cloud, specifically for the Spanner Search team. The team owns the architecture and scale of full-text, vector, and hybrid search capabilities within the Spanner engine. The manager will drive the technical roadmap, engage with customers, manage and mentor a team of software engineers, and collaborate with various partner teams. The role requires experience in software development, building infrastructure, distributed systems, core database internals, people management, and experience with vector search, full-text search, or query processing. Preferred qualifications include experience with large-scale search systems, information retrieval pipelines, vector databases, and database engine internals.

What you'd actually do

  1. Drive the technical roadmap for Spanner’s search capabilities bridging the gaps with competitors and ensuring a seamless integration of full-text and vector search with Spanner’s query engine.
  2. Engage directly with key infra and enterprise customers to deeply understand their use cases, ensure smooth adoption of Spanner's search capabilities, and transform customer feedback into a clear search roadmap.
  3. Manage, mentor, and grow a team of talented software engineers, fostering innovative and high-performance engineering culture.
  4. Work with multiple functions and partner teams including the Spanner Indexing engineering team and also Product Managers, SRE, Engineering Productivity, UI, docs, and client libraries.
  5. Communicate effectively to executive stakeholders including L8+ Team Leads, Directors and VPs who are keenly interested in this space.

Skills

Required

  • software development
  • building infrastructure
  • distributed systems
  • core database internals
  • people management
  • vector search
  • full text search
  • query processing

Nice to have

  • large-scale search systems
  • information retrieval pipelines
  • vector databases
  • database engine internals
  • query execution
  • query optimization
  • distributed data storage
  • architectural trade-offs
  • performance
  • latency
  • scale
  • enterprise cloud environment

What the JD emphasized

  • vector search
  • full text search
  • query processing
  • vector search
  • full text search
  • query processing
  • vector databases