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.