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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