Principal Product Manager

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

Product Manager for the IQ Team (Ontology & Semantic Layer) within Azure Data, focusing on building the semantic backbone that gives AI agents context to reason about business. The role involves defining and delivering the semantic layer vision for Fabric, driving AI-native scenarios by grounding agents in live business operations, and leading end-to-end product strategy and execution for AI solutions on the Fabric platform.

What you'd actually do

  1. Define and deliver the semantic layer vision for Fabric, shaping how customers model business operations into a graph-powered, actionable ontology that bridges analytics and operations.
  2. Drive AI-native scenarios by grounding agents in live business operations, leveraging graph context to improve accuracy, reduce hallucinations, and enable trusted, autonomous decision-making.
  3. Lead end-to-end product strategy and execution, from market insight and ideation through delivery and lifecycle management, ensuring high-quality, compliant, and differentiated AI solutions on the Fabric platform.
  4. Customer adoption and journey, defining a phased path from unified data to unified semantic layers, amplifying existing analytics investments while unlocking operational intelligence and read/write agent scenarios.
  5. Orchestrate cross-org alignment as well as across engineering, research, design, and platform teams (data, analytics, real-time, AI) to deliver a cohesive, differentiated semantic and AI stack.

Skills

Required

  • Bachelor's Degree AND 8+ years’ experience in product/service/project/program management or software development or equivalent experience.

Nice to have

  • 10+ 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, and graph models, with hands-on experience modeling 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.

What the JD emphasized

  • AI agents
  • grounding agents
  • semantic layer
  • ontology
  • AI solutions

Other signals

  • semantic backbone for AI agents
  • grounding AI agents in business context
  • unified ontology for AI understanding
  • AI-native scenarios