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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