Director, Knowledge Graph & Semantics - HYBRID ROLE
Vertex Pharmaceuticals
August 31, 2026
Remote friendly (Boston, MA)
United States
IT
Key Duties And Responsibilities
- Design, build, and operate an enterprise knowledge graph across clinical, research, regulatory, and commercial domains (ingestion, storage, query, lifecycle).
- Build and govern an enterprise semantic layer for consistent metrics, dimensions, entities, and relationships used by AI agents.
- Define graph/semantic strategy, including technology selection and unified architecture.
- Partner with ontology/data modeling to translate domain ontologies into the graph with cross-domain consistency.
- Develop graph traversal and retrieval interfaces (pattern queries, semantic search, graph-aware retrieval for RAG).
- Onboard applications/systems into the knowledge graph and semantic layer.
- Own production operations: SLAs, observability, query performance, cost, and continuous improvement.
Knowledge And Skills (Required)
- 10+ years data engineering/AI-ML/advanced analytics; 3+ years in knowledge graphs, semantic technologies, or enterprise data modeling at scale.
- Hands-on enterprise knowledge graph expertise (schema, ingestion, query, traversal; property/RDF/hybrid + vectors trade-offs).
- Semantic layer leadership (e.g., dbt Semantic Layer, Cube, AtScale, LookML, or comparable).
- Snowflake and/or Databricks experience.
- Cross-domain data integration (entity resolution, master data, lineage).
- Understanding of AI agent/RAG grounding and graph-aware retrieval.
- Production operations experience and leadership/communication with executives.
Preferred
- Pharma/life sciences experience; familiarity with clinical/regulatory/commercial domains.
- GxP and 21 CFR Part 11; validated-system constraints.
- 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).
Pay Range
- $216,400 - $324,600
- Design, build, and operate an enterprise knowledge graph across clinical, research, regulatory, and commercial domains (ingestion, storage, query, lifecycle).
- Build and govern an enterprise semantic layer for consistent metrics, dimensions, entities, and relationships used by AI agents.
- Define graph/semantic strategy, including technology selection and unified architecture.
- Partner with ontology/data modeling to translate domain ontologies into the graph with cross-domain consistency.
- Develop graph traversal and retrieval interfaces (pattern queries, semantic search, graph-aware retrieval for RAG).
- Onboard applications/systems into the knowledge graph and semantic layer.
- Own production operations: SLAs, observability, query performance, cost, and continuous improvement.
Knowledge And Skills (Required)
- 10+ years data engineering/AI-ML/advanced analytics; 3+ years in knowledge graphs, semantic technologies, or enterprise data modeling at scale.
- Hands-on enterprise knowledge graph expertise (schema, ingestion, query, traversal; property/RDF/hybrid + vectors trade-offs).
- Semantic layer leadership (e.g., dbt Semantic Layer, Cube, AtScale, LookML, or comparable).
- Snowflake and/or Databricks experience.
- Cross-domain data integration (entity resolution, master data, lineage).
- Understanding of AI agent/RAG grounding and graph-aware retrieval.
- Production operations experience and leadership/communication with executives.
Preferred
- Pharma/life sciences experience; familiarity with clinical/regulatory/commercial domains.
- GxP and 21 CFR Part 11; validated-system constraints.
- 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).
Pay Range
- $216,400 - $324,600