Principal Product Manager, Data Intelligence and AI Governance

**The Opportunity** Adobe's data platform is built on a set of deeply connected infrastructure products that together form a vertical data intelligence stack. Raw signals are governed and routed at the point of ingestion, enriched and catalogued into curated enterprise definitions, and ultimately transformed into retrieval-ready, agent-optimized knowledge assets. AI agents, self-service analytics, and enterprise decision systems consume the output of this stack every day. The Principal PM, Data Intelligence & AI Governance owns the strategic direction and implementation for the cross-cutting concerns that make this stack trustworthy and agent-ready: metadata strategy, governance frameworks, data lineage, and the readiness of enterprise data assets for AI consumption. This is not a feature PM role. It is a platform-level position responsible for the quality, coherence, and trustworthiness of data as it moves through each layer of the platform — and for the unified operator experience that makes that trust visible! **What you will own** **Metadata Strategy** - Define and drive Adobe's enterprise metadata model — what gets catalogued, how it is structured, what it means, and how it stays current across systems. - Own the product roadmap for metadata enrichment, normalization, and publication — including benchmark definitions, event schemas, data job lineage, and entity relationships. - Partner with the Metadata System PM to translate the metadata strategy into prioritized product features and a coherent data model. - Establish metadata standards that external teams (product analytics, ML, BI) can build on with confidence. **Governance & Agent Readiness** - Own the product definition of 'agent-ready data' — the governance, freshness, lineage, and trust properties - Define the cross-product impact analysis capability: surfacing what breaks across the full stack when an event schema changes, a benchmark definition is updated, or a knowledge entity is deprecated. - Develop and drive the agent readiness scoring model: a composite, per-agent health score that spans signal quality, metadata integrity, and knowledge freshness. - Define the HITL (human-in-the-loop) governance framework across products — what triggers a human review, who reviews it, and how corrections propagate downstream. - Partner with individual product teams to ensure retrieval APIs, MCP integrations, and embedding pipelines are built on governed, trustworthy foundations. **Unified Console & Operator Experience** - Drive the product strategy for a unified operator console that spans across multiple systems— replacing separate registry UIs with a single, coherent governance and observability surface. - Define the cross-layer views that operators need: dependency graphs, freshness dashboards, agent readiness panels, and HITL correction workflows. **What we are looking for** **Required** - 10+ years of product management experience, with at least 3 years in data platform, data infrastructure, or enterprise data products. - Bachelor's degree in Computer Science, Engineering, or a related field - Demonstrated experience owning a data governance, metadata, or data quality product — not just participating in one. - Deep familiarity with the AI/ML data lifecycle: how models consume data, what makes data 'agent-ready,' and where trust breaks down in practice. - Ability to write engineering PRDs that translate complex technical systems into clear user problems, prioritized features, and measurable outcomes. - Ability to design quick prototypes through vibe-coding (preferably Claude code) - Track record of driving cross-functional alignment across engineering, data science, and platform teams without direct authority. - Strong systems thinking — able to reason about a data platform as a causal chain, not a collection of independent features. **Preferred** - Experience with event streaming, schema registries, or data pipeline governance (e.g. Kafka, Databricks, Unity Catalog). -…