Knowledge Graph Engineer / Ontologist

Dir Data Engineering - GE06AE
We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.

The Ontologist (Knowledge Graph Engineer) is responsible for designing, implementing, and operationalizing enterprise semantic models, ontologies, and knowledge representations that provide meaning, context, and explainability for AI-driven analytics, agentic systems, and decision automation.

This role ensures that business concepts, entities, relationships, and behaviors are explicitly modeled and consistently applied across data products and AI systems, enabling reuse, trust, reasoning, and scalable AI adoption across Customer, Operations, and Enterprise domains. This role is part of the Customer Data Ecosystem (CDE) and operates at the intersection of business semantics, data architecture, and AI enablement, translating complex domain knowledge into production-ready semantic assets that are consumable by both humans and machines.

This role can have a Hybrid or Remote work schedule. Candidates who live near one of our office locations will have the expectation of working in an office 3 days a week (Tuesday through Thursday) Candidates who do not live near an office will have a remote work arrangement, with the expectation of coming into an office as business needs arise. Candidates must be eligible to work in the US without company sponsorship.

Primary Job Responsibilities

• 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.

• 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.

• Bachelor's or Master's degree in Computer Science, Engineering, or related field, or equivalent work experience.