Data Modeler - Ontology at Fractal Analytics, Bengaluru
Data Modeler - Ontology at Fractal Analytics in Bengaluru. Full-time. Apply with swipeandhired.
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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
- 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 .
- 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.
- 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.
- 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.
- 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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