Director, Knowledge Graph & Semantics - HYBRID ROLE

Vertex Pharmaceuticals•Boston, MA

6d•Hybrid

About The Position

This is a Hybrid position requiring 3 days a week in our Boston office. We are seeking an experienced engineering leader to build and operate Vertex's enterprise Knowledge Graph and Semantic Layer: the unified, navigable representation of Vertex's data, concepts, and relationships that AI agents and analytical systems traverse to reason over our business. Vertex Pharmaceuticals is in a transformational period, and the AI team is at the center of our AI-first strategy, delivering AI solutions that empower executives, researchers, and business users to make faster, more confident decisions. As part of the Vertex Data Engineering team, you will lead the Knowledge Graph & Semantics function within Knowledge & Grounding. Your team builds the graph that connects Vertex across clinical, research, regulatory, and commercial domains, and the AI semantic layer that defines the business meaning, metrics, and dimensions that sit on top of it. The capability you build becomes the substrate that AI agents traverse to ground their reasoning, and that analytical systems use to answer cross-domain questions consistently across the enterprise. As the Director of Knowledge Graph & Semantics, you will define the graph and semantic strategy for Vertex, select the underlying technology stack, and lead the engineering team that brings it to life. The capability you build will accelerate every downstream AI and analytics initiative at Vertex by giving them a single, governed, traversable model of how Vertex's data fits together.

Requirements

Proven Experience: 10+ years of experience in data engineering, AI/ML, or advanced analytics, with 3+ years specifically focused on knowledge graphs, semantic technologies, or enterprise data modeling at scale.
Knowledge Graph Expertise: Deep hands-on experience designing and operating enterprise knowledge graphs, including schema design, ingestion, query, and traversal patterns. Familiarity with multiple graph paradigms (property graph, RDF/semantic web, hybrid graph + vector approaches) and the trade-offs between them.
Semantic Layer Expertise: Strong experience building and governing semantic layers (e.g., dbt Semantic Layer, Cube, AtScale, LookML, or comparable) that serve analytics and AI consumers consistently.
Cloud Data Platforms: Strong experience with Snowflake and/or Databricks in enterprise environments, including how graph and semantic capabilities integrate with these platforms.
Cross-Domain Data Integration: Track record of integrating data across multiple business domains, including entity resolution, master data, and lineage at enterprise scale.
AI & Agent Grounding: Working understanding of how knowledge graphs and semantic layers are consumed by AI agents and RAG systems, including graph-aware retrieval and traversal for agent reasoning.
Production Operations: Track record of operating graph and analytical systems in production with high availability, query performance, and continuous improvement.
Leadership & Communication: Proven ability to lead technical teams, communicate with executive stakeholders, and translate business needs into graph and semantic models.

Nice To Haves

Preferred Pharma or Life Sciences Context: Experience in pharmaceutical, biotech, or life sciences data environments. Familiarity with clinical, regulatory, and commercial data domains is a plus.
Regulated Environment Experience: Working knowledge of GxP, 21 CFR Part 11, and validated-system constraints on data and AI deployments.
Life Sciences Ontologies: Familiarity with industry ontologies and standards (e.g., SNOMED CT, MedDRA, LOINC, RxNorm, CDISC, IDMP) and how they map into enterprise graph models.
Graph Query Languages: Hands-on experience with Cypher, SPARQL, Gremlin, GQL, or comparable graph query languages.
LLM Integration with Graphs: Experience with text-to-Cypher, text-to-SPARQL, or other LLM-driven graph query generation, and graph-augmented retrieval for AI systems.

Responsibilities

Enterprise Knowledge Graph: Design, build, and operate Vertex's enterprise knowledge graph spanning clinical, research, regulatory, and commercial domains, including ingestion, storage, query, and lifecycle management of nodes, edges, and properties.
Semantic Layer: Build and govern the enterprise semantic layer that enables metrics, dimensions, business entities, and relationships in a single, consistent model used by AI agents.
Graph and Semantic Strategy: Define Vertex's strategy for graph and semantic platform, including technology selection (graph database, AI semantic layer tooling, query API) and the architecture that unifies them.
Ontology Partnership: Partner with the ontology and data modeling function to translate domain ontologies into the graph, ensuring fidelity to source models and consistency across domains.
Agent Traversal & Retrieval: Build the graph traversal and retrieval interfaces that AI agents and other consumers use to ground their reasoning, including pattern queries, semantic search over graph context, and graph-aware retrieval for RAG systems.
Application & System Onboarding: Partner with application and system owners across Vertex to onboard their systems into the enterprise knowledge graph and semantic layer.
Production Operations: Own SLAs, observability, query performance, cost, and continuous improvement for the graph and semantic layer in production.