Staff Software Engineer, Enterprise Data Platform and Governance

Google Google · Big Tech · San Jose, CA +1

Staff Software Engineer role focused on architecting and leading the technical strategy for enterprise data platforms and governance, specifically bridging centralized platforms with cross-domain business environments. The role involves defining how to build high-integrity semantic engines and agentic endpoints for autonomous AI workflows, and architecting frameworks for scaling domain-specific data engineering successes. It requires technical leadership, mentorship, and ensuring privacy, compliance, trust and safety, and responsible AI practices.

What you'd actually do

  1. Define the technical roadmap, engineering standards, and integration patterns for embedded resources across multiple business domains to accelerate high-impact outcomes.
  2. Lead the technical design and execution of secure data onboarding workflows, knowledge graphs, and agentic endpoints capable of sustaining autonomous AI reasoning without human mitigation.
  3. Build technical frameworks that seamlessly harvest domain-specific data engineering successes and contribute them back as reusable, centralized features.
  4. Partner with Group Product Managers and Principal Business Strategy Analysts (BSAs) to solve complex, cross-domain data fragmentation hurdles, balancing localized velocity and business autonomy with rigid data substrate standards.
  5. Serve as the ultimate technical authority for privacy, compliance, trust and safety, and responsible AI practices across forward-deployed systems, ensuring infrastructure is secure by design.

Skills

Required

  • programming in C++, Java, Python, Kotlin or Go
  • testing, and launching software products
  • software design and architecture
  • integrating generative AI tools or Large Language Model (LLM) interfaces into workflows

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field
  • data structures and algorithms
  • technical leadership role leading project teams and setting technical direction
  • working in a complex, matrixed organization involving cross-functional, or cross-business projects
  • semantic modeling
  • knowledge graph construction
  • large-scale data pipeline engineering
  • deploying production-grade AI/ML infrastructure
  • advanced Retrieval-Augmented Generation (RAG) frameworks
  • model evaluation pipelines
  • agentic orchestration

What the JD emphasized

  • agentic endpoints capable of sustaining autonomous AI reasoning without human mitigation
  • integrating generative AI tools or Large Language Model (LLM) interfaces into workflows
  • advanced Retrieval-Augmented Generation (RAG) frameworks, model evaluation pipelines, agentic orchestration

Other signals

  • architecting core frameworks for AI
  • defining technical strategy for AI systems
  • leading technical design and execution of agentic endpoints
  • integrating generative AI tools or LLM interfaces