About the job
Microsoft’s Azure Data engineering team is leading the transformation of analytics in the world of data with products like databases, data integration, big data analytics, messaging & real-time analytics, and business intelligence. The products our portfolio include Microsoft Fabric, Azure SQL DB, Azure Cosmos DB, Azure PostgreSQL, Azure Data Factory, Azure Synapse Analytics, Azure Service Bus, Azure Event Grid, and Power BI. Our mission is to build the data platform for the age of AI, powering a new class of data-first applications and driving a data culture. Within Azure Data, the messaging and real-time analytics team provides comprehensive solutions and a robust platform that enables users to ingest high granularity signals (real-time & observability) and complex data, converting those into a competitive advantage in real-time for both end users and modern applications. IQ Team – Ontology & Semantic Layer: We build the semantic backbone that gives AI agents the context they need to reason about business. The IQ team helps organizations define a unified ontology — a living, structured description of how their business works: its entities, relationships, processes, and rules. By bridging raw data and business meaning, we enable AI agents to understand not just what the data says, but what it means to powering grounded, trustworthy, and actionable intelligence at scale.
Responsibilities
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. 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. 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.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.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. Engage customers and ecosystem to validate product-market fit, shape the competitive narrative, and translate market trends and feedback into prioritized roadmap decisions. Embody our culture and values.
Qualifications
Minimum
Bachelor's Degree AND 8+ years’ experience in product/service/project/program management or software development or equivalent experience. Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred
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, partners, and ecosystem players to validate product-market fit and shape the category narrative.