Job Description

We are seeking a Senior Ontology Data Modeler with 5+ years of experience in data modeling, data architecture, ontology modeling, and semantic modeling. The ideal candidate will have hands-on experience designing enterprise ontologies, semantic data models, knowledge graphs, and business vocabularies, with strong SQL and cloud data platform experience.

Must-Have Technical Skills

• 5+ years of experience in Data Modeling, Data Architecture, and Ontology/Semantic Modeling.
• Strong experience with Conceptual, Logical, and Physical Data Modeling.
• Hands-on experience with RDF, RDFS, OWL, and semantic technologies.
• Experience designing and developing Knowledge Graphs.
• Strong SQL skills.
• Experience with AWS and cloud data platforms.
• Experience with Amazon S3 and familiarity with Apache Iceberg is a plus.
• Insurance domain experience, preferably Annuity.
• Experience translating business requirements into scalable data and semantic solutions.
• Strong stakeholder management and communication skills.

Preferred Technical Skills

• Timbr or other ontology-based semantic-layer platforms.
• Graph databases such as Amazon Neptune, Stardog, or Neo4j.
• Data governance and metadata platforms such as Collibra, Alation, or Microsoft Purview.
• Tableau, Power BI, or Business Objects.
• ETL pipelines and stored procedures.
• AI/GenAI, GraphRAG, Semantic Search, Knowledge Graphs, or Agentic AI.
• Traditional data modeling tools such as ERwin, ER/Studio, or PowerDesigner.
• Git and version-controlled development workflows.

Key Responsibilities

• Design, build, and maintain enterprise ontologies, semantic data models, knowledge graphs, taxonomies, and business vocabularies.
• Define business entities, relationships, hierarchies, metrics, and semantic rules across enterprise data domains.
• Model insurance domains including Policy, Claims, Underwriting, Customer, Product, Sales, and Producer/Agency.
• Harvest business logic from reports, dashboards, ETL processes, and stored procedures.
• Work with business SMEs to capture and formalize business knowledge.
• Apply both bottom-up and top-down modeling approaches.
• Refine, validate, and improve AI-assisted ontology candidates.
• Map ontology concepts to physical data sources and validate results against source-of-truth systems.
• Implement ontology development using versioning, testing, and controlled promotion across DEV → QA → STAGE → PROD.
• Manage ontology and semantic-model artifacts using Git.
• Collaborate with Data Architects, Data Engineers, BI teams, AI teams, and business SMEs.
• Support BI, analytics, AI, and agent-based workflows consuming semantic models.
• Support data governance, metadata management, data lineage, and data quality initiatives.
• Ensure semantic solutions align with enterprise architecture, industry standards, and data governance best practices.

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