Senior Ontology Data Modeler

Must Have Technical/Functional Skills:

• 5+ years in Data Modeling, Data Architecture, and Ontology/Semantic Modeling
• Strong experience in conceptual, logical, and physical data modeling
• Hands-on experience with ontology and semantic modeling using RDF, RDFS, OWL, or related semantic technologies, including knowledge-graph design
• Proficiency with SQL and experience modeling on modern cloud data platforms (AWS; familiarity with object storage and open table formats such as S3/Apache Iceberg is a plus)
• Insurance domain experience (Annuity preferred)
• Experience translating business requirements into scalable data and semantic solutions, working directly with business stakeholders
• Strong analytical, communication, and stakeholder-management skills - able to bridge business and technical teams

Preferred Qualifications:

• Hands-on experience with an ontology-based semantic-layer platform (e.g., Timbr) and/or graph databases such as Amazon Neptune, Stardog, or Neo4j
• Familiarity with data governance/metadata platforms such as Collibra, Alation, or Microsoft Purview
• Experience harvesting semantics from existing BI assets (Tableau, Power BI, Business Objects) and ETL pipelines
• Exposure to AI/GenAI, GraphRAG, semantic search, or enterprise knowledge-graph and agentic-AI initiatives
• Familiarity with traditional data-modeling tools (ERwin, ER/Studio, PowerDesigner)

Roles & 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 existing business logic from reports, dashboards, and ETL/stored procedures; capture tacit knowledge through structured sessions with business SMEs
• Apply a hybrid modeling approach (bottom-up from source schemas, top-down from business concepts), including refining and validating AI-assisted ontology candidates
• Map ontology concepts to physical data sources and validate model outputs against source-of-truth systems and existing reports
• Treat ontology development like application delivery - versioning, testing, and controlled promotion through DEV QA STAGE PROD, with artifacts managed in Git
• Collaborate with Data Architects, Data Engineers, and business SMEs; support consuming teams across BI, analytics, and AI/agent workflows
• Support data governance, metadata management, data lineage, a nd data quality initiatives
• Ensure alignment with enterprise architecture, industry standards, and data governance best practices