Senior Ontology Data Modeler

Job Description

Senior Ontology Data Modeler

Location: West Des Moines, IA

Fulltime

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