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!

• 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.

• 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.

• 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.

• 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.

• 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…