Principal Ontologist, Graph Modeler

The Hartford

. Lead the design and execution of enterprise-scale semantic layers for the Business Insurance data portfolio to standardize business meaning and enable trusted analytics, AI, and agentic use cases.

Posted 10/27/2026full-timeRemote • United StatesLead💰 $145,440 - $218,160 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in semantic layer architecture, ontology modeling, and knowledge graph design, with a strong focus on integrating AI and analytics within the Business Insurance domain. Proven ability to lead enterprise-scale initiatives that enhance data governance, explainability, and decision intelligence.

Highest-signal resume keywords

Semantic Layer ArchitectureOntology ModelingKnowledge Graph DesignRDF, OWL, SPARQLBusiness Insurance Domain Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Graph ModelingKnowledge Graph DesignSemantic InteroperabilityData GovernanceContext-Aware Data EngineeringAI Consumption PatternsCanonical ModelingEnterprise Data PlatformsLarge Language Models IntegrationTriple-Store Technologies

Soft Skills

Communication SkillsPresentation SkillsCollaboration SkillsTechnical Leadership

Tools & Technologies

Neo4jAmazon NeptuneSpanner GraphAWSGCPSnowflake

Industry Keywords

Business InsuranceUnderwritingPolicy ManagementClaims ManagementPortfolio Management

Tech Stack

Tools & technologies

AWSGoogle Cloud PlatformNeo4jSnowflake

About the role

Key responsibilities & impact

Lead the design and execution of enterprise-scale semantic layers for the Business Insurance data portfolio to standardize business meaning and enable trusted analytics, AI, and agentic use cases.
Define and operationalize ontologies, context graphs, and knowledge graphs across Business Insurance domains to power reasoning, explainability, and decision intelligence.
Model core insurance concepts and relationships spanning customers, accounts, submissions, quotes, policies, coverages, products, risks, claims, producers, and portfolio performance.
Enable semantic-first AI and agentic analytics by ensuring large language models and agents can consume governed business context, metrics, rules, and relationships.
Define canonical semantic vocabularies that standardize meaning across structured and unstructured Business Insurance data sources.
Drive production-scale execution of semantic and graph platforms with standards for performance, governance, security, and lifecycle management.
Define and promote standards and reusable patterns for ontology modeling, graph modeling, semantic interoperability, and ontology reuse across delivery teams.
Partner with Business Insurance data product owners, domain leaders, architects, and engineers to embed semantic models into data products, AI pipelines, analytics, and activation layers.
Work closely with AI Data Architects and AI Data Engineers to operationalize ontologies through graphs, metadata services, APIs, and production data platforms.
Align ontologies and graph models with enterprise data governance, metadata, lineage, quality, privacy, and security standards.
Enable explainability by ensuring AI and analytical outputs can be traced to governed semantic definitions, relationships, rules, and source data.
Serve as the principal authority for semantic engineering, ontology, and graph-modeling practices supporting the Business Insurance data portfolio.
Advance agentic data engineering through reference patterns, playbooks, proofs of value, and production deployments.
Contribute to communities of practice, reference guidance, reusable assets, and internal enablement materials.

Requirements

What you’ll need

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Information Management, or a related field, or equivalent work experience with large scale data warehouses data.
8–12+ years of hands-on experience in semantic layer architecture, ontology modeling, graph modeling, and knowledge graph design at enterprise scale.
Deep, hands-on expertise with RDF, OWL (OWL 2), RDFS, SKOS, SPARQL, and W3C semantic web standards.
Proven experience designing and operating enterprise knowledge graphs and governed semantic assets.
Hands-on experience with graph or triple-store technologies such as Neo4j, Amazon Neptune, or Spanner Graph.
Experience integrating knowledge graphs with large language models, retrieval-augmented generation pipelines, vector stores, and agentic frameworks.
Strong understanding of AI consumption patterns, including embeddings, grounding, reasoning, and explainability.
Experience integrating semantic layers with data platforms, APIs, metadata systems, analytics platforms, and AI pipelines.
Ability to translate complex insurance domain knowledge into formal, machine-readable semantic and graph structures.
Strong understanding of context-aware data engineering, canonical modeling, and semantic interoperability.
Proven ability to move from strategy to pilot to scaled enterprise capability.
Strong executive influence and thought leadership in semantic engineering, agentic analytics, and AI-native data engineering.
Hands-on experience with AWS, GCP, and Snowflake.
Business Insurance domain knowledge across one or more areas such as underwriting, policy, claims, distribution, product, customer, or portfolio management.
Excellent communication, presentation, collaboration, and technical leadership skills.
Candidate must be authorized to work in the US without company sponsorship.
The company will not support the STEM OPT I-983 Training Plan endorsement for this position.

Benefits

Comp & perks

Short-term or annual bonuses
Long-term incentives
On-the-spot recognition
Perks & Benefits