Senior Product Manager, Data Platform - Knowledge & Retrieval

Adobe•San Jose, CA

About The Position

Adobe's data platform is a vertical data intelligence stack where raw signals are governed, routed, enriched, and catalogued into curated enterprise definitions. These definitions are then transformed into retrieval-ready, agent-optimized knowledge assets. The knowledge tier is the top of this stack, ensuring curated definitions become trustworthy knowledge consumed by AI agents, self-service analytics, and enterprise decision systems. As the Senior Product Manager for this layer, you will own its execution throughout the entire process, including how knowledge assets are modeled, published, maintained, and delivered to consuming agents via retrieval and integration interfaces. This role involves turning a multi-quarter roadmap into shipped capabilities, collaborating closely with engineering, data science, and peer platform PMs. It is a builder's role focused on Adobe's agent-ready data strategy.

Requirements

7+ years of product management experience, including 2+ years owning a data platform, data infrastructure, or enterprise data product end to end — not just contributing to one.
Working knowledge of knowledge-graph concepts, embedding pipelines, RAG architectures, MCP servers, and agent skills, plus a working understanding of how agents and models consume data and what makes an asset "agent-ready."
Experience with human-in-the-loop quality and correction workflows in production data systems.
Ability to write engineering PRDs that translate complex technical systems into clear user problems, prioritized features, and measurable success metrics and guardrails.
Ability to design and build quick prototypes through vibe-coding (Claude Code preferred) to de-risk decisions before engineering invests.
Able to reason about the platform as a causal chain rather than independent features, and to articulate how a decision drives adoption, retention, and downstream platform value.
Proven ability to drive cross-functional delivery across engineering and program through influence rather than authority, with comfort operating at ambiguous (roadmap) and precise (specification) altitudes simultaneously.

Responsibilities

Own the product roadmap — develop and drive a 6–12 month roadmap for knowledge-asset modeling, retrieval APIs, agent-integration interfaces, and embedding/RAG pipelines, prioritizing and re-prioritizing against shifting platform needs.
Lead delivery from inception to launch — provide project leadership and day-to-day management spanning engineering and build, making thoughtful quality / customer-value / time-to-market tradeoffs and mitigating risk.
Own knowledge freshness and lifecycle — define how curated definitions are ingested, versioned, deprecated, and kept current, so consuming agents never retrieve stale or orphaned knowledge.
Deliver the trust signals for the knowledge layer — freshness, lineage, and quality — so knowledge assets can be safely consumed by agents, while contributing these signals to the platform-wide agent readiness score.
Build human-in-the-loop correction workflows at the knowledge layer — define what triggers human review of a knowledge asset, and how corrections propagate to downstream consumers.
Write clear engineering PRDs — translate platform requirements into prioritized features, crisp specifications, and concrete performance measures and safety thresholds.
Partner across the platform team — align with peer PMs owning metadata, event telemetry, and cross-cutting governance so retrieval and embedding surfaces are built on governed, trustworthy foundations.
Prototype to think — use vibe-coding (Claude Code preferred) to stand up quick, testable prototypes that reduce risk in build choices ahead of engineering dedication.