Senior Product Manager, Data Platform - Knowledge & Retrieval

Adobe Adobe · Enterprise · San Jose, CA

Senior Product Manager for Adobe's data platform, focusing on the knowledge tier that transforms curated definitions into retrieval-ready, agent-optimized knowledge assets. Owns the roadmap, delivery, lifecycle, trust signals, and human-in-the-loop correction workflows for these assets, enabling consumption by AI agents and other systems. Requires strong product management experience, AI knowledge-stack fluency (including RAG and agent readiness), and experience with quality/correction workflows.

What you'd actually do

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • 7+ years of product management experience
  • 2+ years owning a data platform, data infrastructure, or enterprise data product end to end
  • Working knowledge of knowledge-graph concepts
  • Working knowledge of embedding pipelines
  • Working knowledge of RAG architectures
  • Working knowledge of MCP servers
  • Working knowledge of agent skills
  • Working understanding of how agents and models consume data
  • Working understanding of 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
  • Ability to design and build quick prototypes
  • Systems thinking
  • Business judgment
  • Cross-functional influence

Nice to have

  • vibe-coding (Claude Code preferred)

What the JD emphasized

  • owning a data platform, data infrastructure, or enterprise data product end to end
  • working knowledge of knowledge-graph concepts, embedding pipelines, RAG architectures, MCP servers, and agent skills
  • 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
  • reason about the platform as a causal chain rather than independent features
  • articulate how a decision drives adoption, retention, and downstream platform value
  • drive cross-functional delivery across engineering and program through influence rather than authority
  • comfort operating at ambiguous (roadmap) and precise (specification) altitudes simultaneously

Other signals

  • AI agents
  • knowledge assets
  • retrieval-ready
  • agent-optimized
  • embedding/RAG pipelines