Data Modeler - Ontology

Data Modeler - Ontology

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Job Description

About the Role

We are looking for a hands-on Ontology / Knowledge Graph Engineer who understands not just graph databases, but the true semantic foundations behind enterprise knowledge graphs. This role requires experience in building domain ontologies, modeling conceptual semantics, and connecting those models to enterprise data sources.

This is not a Cypher-only graph querying role.
This is a role for someone who has worked with OWL/RDF, domain modeling, and semantic standards—and can translate complex insurance concepts into a formal ontology that powers AI, LLMs, data governance, and enterprise knowledge systems.

Key Responsibilities

1. Ontology Design & Semantic Modeling

• Design, model, and maintain insurance domain ontologies using OWL/RDF.
• Build conceptual and logical semantic models for entities such as:
  • Policies, Coverages, Perils, Claims, Loss Events, Parties, Risk Objects, Brokers/Agents.
• Define canonical data definitions, taxonomies, hierarchical relationships, and semantic constraints.
• Translate business domain models and regulatory structures into formal semantic structures.

2. Knowledge Graph Architecture

• Design and extend production-grade knowledge graphs derived from ontologies.
• Make design decisions on:
  • How the ontology becomes the semantic index for enterprise data.
  • How graph nodes point back to source systems through schema mapping.
  • How the KG will be used in RAG, LLM context grounding, or reasoning pipelines.
• Implement ontology-driven schemas using RDF stores or labeled-property graph stores.

3. Source System Mapping

• Parse schemas from policy admin, claims, CRM, billing, and underwriting systems.
• Map source fields to ontology entities using:
  • Schema parsing
  • Semantic mapping
  • Transformation rules
• Ensure semantic alignment and consistency across diverse insurance systems.

4. Azure Cloud Integration

• Deploy and operationalize ontology and KG components using:
  • Azure Data Lake / Storage
  • Azure Purview / Microsoft Fabric Catalog
  • Azure Cognitive Search (for semantic indexing)
  • Azure Kubernetes Services / Azure Functions (for KG pipelines)
  • Azure OpenAI (for semantic enrichment, RAG, and reasoning)
• Integrate KG metadata into Azure metadata and lineage systems.

5. Collaboration & Governance

• Work closely with:
  • Underwriting SMEs
  • Claims SMEs
  • Data governance teams
  • Data engineering teams
  • AI/ML product teams
• Document semantic models, maintain change logs, and drive ontology versioning.

Required Skills (Non-Negotiable)

• Hands-on experience in ontology development, not just graph querying.
• Strong knowledge of:
  • OWL, RDF, RDFS, SKOS
  • SPARQL, SHACL (preferred)
• Experience designing conceptual and logical domain models.
• Demonstrated ability to build a knowledge graph from scratch, not just use one.
• Strong insurance domain understanding:
  • Policy structures
  • Exposure / risk modeling fundamentals
  • Claims life cycle
  • Product hierarchies
  • Regulatory constraints
• Ability to map enterprise source schemas to semantic models.
• Experience with at least one semantic technology stack:
  • Stardog, GraphDB, Neo4j (with RDF frameworks), TopQuadrant, AllegroGraph, etc.

Nice-to-Have Skills

• Experience building LLM-integrated semantic pipelines:
  • Context engineering
  • RAG indexing
  • Metadata grounding
• Familiarity with Azure OpenAI or similar LLM orchestration frameworks.
• Understanding of industry data standards such as ACORD.

Soft Skills

• Strong analytical and conceptual modeling skills.
• Ability to challenge assumptions and define canonical definitions.
• Ability to communicate with business SMEs and translate domain knowledge into ontology structures.
• Structured documentation and versioning discipline.

Why This Role Matters

This role is foundational to:

• Enterprise LLM adoption
• Semantic search
• Knowledge-driven underwriting & claims workflows
• Data governance
• AI safety & consistency
• Eliminating redundant and fragmented data definitions across systems

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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Experience Level

Senior Level