About this opportunity
Techvilla Solutions lists this Senior Ontology Data Modeler opportunity in Grimes, Iowa. Review the employer’s description below for duties, qualifications and application requirements.
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.
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.