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).
• Familiarity with knowledge graph concepts, embedding pipelines, or retrieval-augmented generation (RAG) architectures, MCP server, Skills
• Prior exposure to HITL (human-in-the-loop) quality and correction workflows in production data systems.
• Experience building or operating a unified data catalog — knowledge of tools like Open Metadata, Amundsen, DataHub, Atlan or equivalent.
• Background in a platfo