Director, Knowledge Graph & Semantics - HYBRID ROLE

About the role

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.

Responsibilities

• 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.
• Build and govern the enterprise semantic layer that enables metrics, dimensions, business entities, and relationships in a single, consistent model used by AI agents.
• 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.
• Partner with the ontology and data modeling function to translate domain ontologies into the graph, ensuring fidelity to source models and consistency across domains.
• 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.
• Partner with application and system owners across Vertex to onboard their systems into the enterprise knowledge graph and semantic layer.
• Own SLAs, observability, query performance, cost, and continuous improvement for the graph and semantic layer in production.

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.

Skills

• 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.

Benefits

The role is eligible for an annual bonus and annual equity awards. Some roles may also be eligible for overtime pay, in accordance with federal and state requirements. Actual base salary pay will be based on a number of factors, including skills, competencies, experience, and other job-related factors permitted by law.

Pay

$216,400 - $324,600

Schedule

Hybrid: work remotely up to two days per week; or On-Site: work five days per week on-site with ad hoc flexibility.