Data Modeler - Ontology

Fractal Analytics Inc.; Bengaluru, Hyderabad; On-site; INR 1,500,000 - 2,900,000; Full time

Job summary

Fractal Analytics Inc. is seeking an Ontology/Knowledge Graph Engineer in Bengaluru to design and implement semantic models for enterprise knowledge graphs using OWL/RDF. You will shape conceptual models and connect them to enterprise data sources.

You will build production-grade knowledge graphs, map schemas, and deploy AI-enabled pipelines on Azure, enabling RAG, grounding, and governance across insurers.

Qualifications

• Hands-on experience in ontology development and semantic modeling.
• Strong knowledge of OWL, RDF, RDFS, SKOS; SPARQL; SHACL preferred.
• Experience building and mapping knowledge graphs from scratch.
• Insurance domain knowledge: policies, claims, regulatory constraints.
• Experience with semantic tech stacks (Stardog, GraphDB, Neo4j RDF).

Responsibilities

• Design, model and maintain insurance domain ontologies using OWL/RDF.
• Develop production-grade knowledge graphs and semantic indexing.
• Map source schemas to ontology entities via schema parsing and mapping.
• Integrate KG components with Azure Data Lake, Purview, and OpenAI.
• Collaborate with underwriting, claims, governance teams; document models and versioning.

Skills

Ontology development

OWL/RDF/RDFS/SKOS

SPARQL

Knowledge graph design

Domain modeling

Insurance domain knowledge

Tools

Stardog

GraphDB

Neo4j RDF

TopQuadrant

AllegroGraph

Job description

## Data Modeler - OntologyApply: Bengaluru: Hyderabad: Full time: Posted Today: End Date: March 30, 2027 (30+ days left to apply): SR-40395It's fun to work in a company where people truly BELIEVE in what they are doing!*We're committed to bringing passion and customer focus to the business.*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 systemsIf you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!