Data Modeler - Ontology

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Responsibilities

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

Requirements

• This is not a Cypher-only graph querying role.
• Skills (Non-Negotiable) Hands-on experience in ontology development , not just graph querying.
• 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 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.

Preferred

• 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.
• 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 .
• 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

Benefits

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

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