Director, Knowledge Graph & Semantics - HYBRID ROLE

Key Duties And Responsibilities
- Design, build, and operate an enterprise knowledge graph across clinical, research, regulatory, and commercial domains (ingestion, storage, query, lifecycle management).
- Build and govern an enterprise semantic layer enabling consistent metrics, dimensions, business entities, and relationships for AI agents.
- Define graph and semantic platform strategy, including technology selection (graph DB, semantic tooling, query API) and unifying architecture.
- Partner with ontology/data modeling to translate domain ontologies into the graph with cross-domain consistency.
- Build graph traversal and retrieval interfaces for agent grounding, including pattern queries, semantic search, and graph-aware retrieval for RAG.
- Onboard applications/systems into the graph and semantic layer.
- Own production operations: SLAs, observability, query performance, cost, and continuous improvement.

Knowledge And Skills (Qualifications)
- 10+ years in data engineering, AI/ML, or advanced analytics; 3+ years focused on knowledge graphs/semantic technologies/enterprise data modeling at scale.
- Hands-on knowledge graph expertise (schema, ingestion, query, traversal; property/RDF/hybrid + vector trade-offs).
- Semantic layer expertise (e.g., dbt Semantic Layer, Cube, AtScale, LookML, or comparable).
- Experience with Snowflake and/or Databricks.
- Cross-domain data integration (entity resolution, master data, lineage).
- Understanding of KG/semantic layer usage by AI agents and RAG.
- Production operations experience (availability, performance, continuous improvement).
- Leadership and executive communication.

Preferred
- Pharma/life sciences domain experience.
- GxP and 21 CFR Part 11 knowledge.
- Life sciences ontologies (SNOMED CT, MedDRA, LOINC, RxNorm, CDISC, IDMP).
- Graph query languages (Cypher, SPARQL, Gremlin, GQL, etc.).
- LLM integration with graphs (text-to-Cypher/SPARQL; graph-augmented retrieval).