Senior Product Manager

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Product Management

Product Manager for Microsoft's Azure Data team, focusing on the IQ Team's Ontology & Semantic Layer. This role will build the operational brain for organizations to run their agents by providing a semantic backbone that gives AI agents context for reasoning about business. The goal is to bridge raw data and business meaning to enable AI agents to understand data's meaning, powering grounded, trustworthy, and actionable intelligence at scale.

What you'd actually do

  1. Own a customer problem space within the ontology as the semantic operational context for organizations. This includes customer problem definition and PRD through roadmap execution with engineering, and launch and landing with customers and field.
  2. Drive AI agent grounding scenarios by leveraging ontology to give agents structured business context, improving reasoning accuracy, reducing hallucinations, and enabling trusted, autonomous decision-making.
  3. Translate customer needs into ontology platform features and adoption guidance, defining a phased path from raw data to a unified semantic layer that amplifies existing analytics investments and unlocks operational agent scenarios.
  4. Influence ontology technical architecture and long-term platform strategy in close collaboration with engineering, design and data science.
  5. Drive strategic partnerships and cross-org alignment to unlock new ontology scenarios, amplify impact, and deliver a cohesive semantic and AI stack across Fabric.

Skills

Required

  • Bachelor's Degree AND 5+ years experience in product/service/project/program management or software development OR equivalent experience.

Nice to have

  • 7+ years of experience in product management, or related roles in the AI and data and analytics domain.
  • Proven track record of leading, shipping and delivering complex and innovative Data & AI solutions that create value for customers and stakeholders, using Microsoft AI or similar technologies.
  • Deep data modeling & semantic systems: Experience across relational, dimensional, semantic, ontology, or graph models, with hands-on experience working with business domains (entities, relationships, metrics, events, temporal context).
  • AI + Data Platform Fluency: Deep understanding of enterprise data platforms (warehouse, Lakehouse, semantic layers, metadata systems) and how to ground AI/agents in business semantics to deliver accurate, trustworthy outcomes.
  • End-to-end product execution: Proven ability to lead the full product lifecycle from market insight and ideation through delivery and adoption with rigor in prioritization, quality, compliance, and differentiation.
  • Customer-centric problem solving: Ability to translate ambiguous customer scenarios into clear modeling requirements, technical tradeoffs, and phased adoption journeys that drive measurable customer value.
  • Metadata, governance, and platform depth: Experience with metadata systems, lineage, governance, schema evolution, and data quality, with a pragmatic approach to building scalable, enterprise-ready platforms.
  • Cross-org leadership and external engagement: Ability to drive alignment across engineering, research, and platform teams, while engaging customers, partners, and ecosystem players to validate product-market fit and shape the category narrative.

What the JD emphasized

  • AI agent grounding scenarios
  • ontology
  • semantic layer
  • AI agents
  • operational agent scenarios
  • AI stack

Other signals

  • semantic backbone for AI agents
  • ground AI agents in business context
  • ontology for AI reasoning