Director, Product Management - Data Intelligence Foundation
Job Description
The Director of Product Management, Data Intelligence Foundation is responsible for owning the full product management layer across multiple engineering orgs, including Files/Natives, Ontology/Relationship, Data Capabilities, Knowledge/Metadata, and Query Plane. Key responsibilities include defining the substrate for legal data accessibility, designing an API contract, contributing to engineering scope decisions, and articulating trade-offs in a data consistency model.
Files / Natives: Define the storage primitive for legal documents, images, and native files, ensuring consistent accessibility across all products, partner integrations, and AI workflows with appropriate SLAs, access contracts, and API surfaces.
Ontology / Relationship: Develop the semantic layer that encodes meaning in legal data, enabling AI agents to reason over the data. Define the entities, relationships, and contracts that form the shared vocabulary for legal data.
Data Capabilities: Build the operational backbone that makes the platform observable, auditable, and explainable. Ensure these properties are non-negotiable in legal data intelligence use cases.
Knowledge / Metadata: Evolve the core data model to serve AI application teams as first-class consumers alongside legacy users. Ensure a unified materialized document layer consistent across all workspaces.
Query Plane: Design a performance-sensitive and strategically important service with a rich materialized document layer, standardized ingestion APIs, hybrid retrieval capabilities, and tiered storage.
Minimum Qualifications
12+ years in product management; 5+ years leading platform or infrastructure PM organizations.
Deep fluency with data platform primitives, including storage systems, metadata layers, knowledge graphs, query engines, or equivalent.
Experience building at companies where the data platform is the product, not a supporting system.
Bachelor's degree in Business, Computer Science, Engineering, or Design, or comparable work experience.
Preferred Qualifications
Experience building for AI systems, agents, or ML pipelines as primary consumers.
Understanding of what a model needs from data compared to what a human does, and designing for both.