Knowledge Graph Engineer, Ontologist

The Hartford

Ontologist designing enterprise semantic models and knowledge representations for AI-driven analytics and decision automation. Lead semantic layer architecture and operationalize knowledge graphs in various domains.

Posted 5/27/2026full-timeConnecticut, Illinois, New York, North Carolina, Ohio • 🇺🇸 United StatesSeniorLead💰 $156,000 - $234,000 per yearWebsite

Tech Stack

Tools & technologies

AWSGoogle Cloud PlatformNeo4j

About the role

Key responsibilities & impact

Lead the design and execution of enterprise-scale semantic layers to standardize business meaning and enable trusted analytics, AI, and Agentic use cases.
Define and operationalize ontologies, context graphs, and knowledge graphs across domains to power reasoning, explainability, and decision intelligence.
Enable semantic-first AI and Agentic analytics, ensuring LLMs and agents can consume governed business context, metrics, and rules.
Define canonical semantic vocabularies that standardize meaning across structured and unstructured data sources.
Drive production-scale execution of semantic and knowledge platforms with strong standards for performance, governance, security, and lifecycle management.
Evangelize Agentic Data Engineering, driving adoption through patterns, playbooks, and real-world deployments across the enterprise.
Define and promote standards and best practices for semantic modeling and ontology reuse across delivery teams.
Partner with architects and engineers to embed semantic models into data products, AI pipelines, and activation layers.
Work closely with AI Data Architects and AI Data Engineers to operationalize ontologies into production systems (e.g., via graphs, metadata services, APIs).
Align ontologies with enterprise data governance, lineage, and quality standards.
Enable explainability by ensuring AI outputs can be traced back to governed semantic definitions.
Serve as the enterprise authority on semantic engineering and ontology practices.
Contribute to communities of practice, reference guidance, and internal enablement materials.

Requirements

What you’ll need

8–12+ years of hands-on experience in semantic layer architecture, ontology modeling, and knowledge graph design at enterprise scale.
Deep, hands‑on expertise with RDF, OWL (OWL2), RDFS, SKOS, SPARQL (querying, optimization, semantic analytics), and W3C semantic web standards
Proven experience designing and operating knowledge graphs at enterprise scale
Hands‑on experience with graph or triple‑store technologies (e.g., Neo4j, Neptune, TigerGraph, Spanner Graph)
Experience integrating knowledge graphs with LLMs, RAG pipelines, vector stores, and Agentic frameworks.
Strong understanding of AI consumption patterns, including embeddings, grounding, and explainability
Experience integrating semantic layers with data platforms, APIs, metadata systems, and AI pipelines
Ability to translate complex domain knowledge into formal, machine‑readable semantic structures
Strong understanding of context-aware data engineering and semantic interoperability.
Proven ability to move from strategy → pilot → scaled enterprise capability.
Strong executive influence and thought leadership in Agentic analytics and AI‑native data engineering.
Hands-on experience with AWS, GCP, and Snowflake
Excellent communication, presentation, and leadership skills.

Benefits

Comp & perks

Other rewards may include short-term or annual bonuses
Long-term incentives
On-the-spot recognition

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

semantic layer architectureontology modelingknowledge graph designRDFOWLRDFSSKOSSPARQLgraph technologiescontext-aware data engineering

Soft Skills

executive influencethought leadershipcommunicationpresentationleadership