Product Manager, Enterprise Data Platform

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

Product Manager for Google's Enterprise Data Platform, focusing on evolving it into an AI-ready ecosystem. The role involves building a trusted data substrate to enable autonomous workflows and integrating AI/ML capabilities, RAG, and semantic modeling into the platform. The goal is to transition the infrastructure to an active semantic engine and deliver certified data products for AI.

What you'd actually do

  1. Build and manage an actionable product roadmap for aspects of the Enterprise Data Platform (EDP). Use data-driven arguments to balance current operational needs with the requirements of an agentic, AI-ready future.
  2. Identify and recommend solutions to complex data fragmentation and governance problems. Ideate new features to further integrate and enhance our data platforms capabilities.
  3. Interact with multiple engineering teams as a respected technical peer. Translate goals into detailed PRDs and feature specifications (e.g., NL Query) to power AI-Ready Data.
  4. Leverage a deep understanding of AI/ML, RAG, and semantic modeling to guide engineering teams, accelerate your personal impact, and enhance overall product capabilities.
  5. Take shared accountability for project outcomes by driving product-market fit for advanced capabilities to unlock data for AI. Define success metrics to keep teams focused on the most impactful work.

Skills

Required

  • product management
  • technical role
  • software products full life-cycle
  • integrating generative AI tools
  • Large Language Model (LLM) interfaces
  • AI/ML-driven features
  • AI/ML-driven infrastructure
  • Large Language Models (LLMs)
  • Natural Language Processing (NLP)
  • data pipelines

Nice to have

  • Master's degree in a technology or business related field
  • infrastructure components
  • APIs
  • scale data-intensive platforms
  • prompt engineering
  • model evaluation
  • deploying AI solutions in a corporate/enterprise environment
  • working in a geographically distributed team environment
  • data domain
  • enterprise platforms
  • infrastructure

What the JD emphasized

  • agentic, AI-ready future
  • autonomous workflows
  • AI-Ready Data
  • AI/ML
  • RAG
  • semantic modeling
  • unlock data for AI
  • generative AI tools
  • Large Language Model (LLM)
  • AI/ML-driven features
  • Large Language Models (LLMs)
  • Natural Language Processing (NLP)
  • prompt engineering
  • model evaluation
  • deploying AI solutions

Other signals

  • evolving the Enterprise Data Platform (EDP) to create a data ecosystem that is ready for the next generation of AI
  • building a trusted enterprise data substrate across our business, technical, and data assets to ensure our information is robust for critical operations and certified for safe, autonomous automation
  • delivering capabilities that transition our infrastructure from passive inventory to an active and integrated semantic engine
  • drive the development of high-integrity, certified data products that enable autonomous workflows and resolve complex, cross-domain issues across the enterprise
  • power AI-Ready Data
  • unlock data for AI