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# Director, Product Management - Data Intelligence Foundation

- **Company:** Relativity
- **Location:** Chicago, IL, United States
- **Salary:** $188,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Data Infrastructure, Graph Database, Data Intelligence, Metadata, MongoDB, Core Data, Information Technology, Data Management, Machine Learning Operations, Databricks
- **Published:** August 5, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/87967937/1

## About the Role

* 12+ years in product management; 5+ yearsleading platform or infrastructure PM organizations

 * Deep fluency with data platform primitives, including storage systems, metadata layers, knowledge graphs, query engines, or equivalent. You can design an API contract, contribute to engineering scope decisions, and articulate the trade-offs in a data consistency model.

 * Demonstrated ability to manage and build a PM team through hiring, leveling, and establishing product practice for a new domain

 * Bachelor's degree in Business, Computer Science, Engineering, or Design, or comparable work experience

Preferred qualifications

 * Experience building at companies where the data platform is the product, not a supporting system.Snowflake, Databricks, Elastic, MongoDB, Palantir, and similar are strong indicators of the right background.

 * Experience building for AI systems, agents, or ML pipelines as primary consumers. You understand what a model needs from data that a humandoesn't, and you design for both.

Ways of working

 * Engineering credibility is paramount.You will be in technical discussions with skilled and knowledgeable engineering leaders regularly.You need to be a peer in those conversations, not a relay.

 * Organizational cadence.Youestablishthe operating rhythm for a multi-pillar PM org: the forum structure, planning cadence, and decision frameworks that let the team move with velocity and alignment. When priorities conflict across pillars, you hold the trade-off clearly and resolve it cleanly.

 * Internal GTM ownership.Adoption of the Foundational Layer means every product team at Relativity builds with it. You define how internal teams discover, integrate, and get value from platform services, and you measure it. This is a product strategy job and a change management job simultaneously.

## Description

The full PM layer across multiple engineering orgs (~# engineers):

 * Files / Natives: The storage primitive for legal documents, images, and native files. You define the substrate that makes immutable legal data consistently accessible across every product, partner integration, and AI workflow, with the SLAs, access contracts, and API surface that teams canbuild onwith confidence. The underlying data primitives are the foundation; the degree to which the retrieval layer (Query Plane) matches this structuredetermineshow easily the organization can navigate between "slow data" and "fast data" use cases.

 * Ontology / Relationship: The semantic layer of the Relativity Intelligence Model. Ontology encodesmeaning:what kinds of things exist in legal data and how they relate, so that AI agents can reason, not just query. You define what Relativity's Ontology becomes: the entities, relationships, and contracts that give every Skill and Agent a shared vocabulary for legal data.

 * Data Capabilities: Reporting, Audit, and internal data infrastructure. The operational backbone that makes the platform observable, auditable, and explainable. These are non-negotiable properties in legal data intelligence use cases.

 * Knowledge / Metadata: The core data model that every product team, customer, and integration partner works with. A unified materialized document layer, consistent across all workspaces, is the mandate. Your roadmap evolves this surface to serve AI application teams as first-class consumers alongside the users who have relied on it for years.

 * Query Plane: One of the most performance-sensitive and strategically important services in the product. The mandate is a unified retrieval pillar with a rich materialized document layer: standardized ingestion APIs independent of data source, hybrid retrieval (lexical + vector) with reranking, chunking as a managed capability, and tiered storage (cold/warm/hot).


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