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