Position: Semantic Data Modeler
Candidates need to relocate to Des Moines, Iowa. They are onsite 4 days per week Mandatory – no REMOTE! 1 year contract. Need to be strong in Semantic Modeling.
Hybrid in WDM office - 4 days onsite, 1 day WFH
Currently budgeted through 10/11/2027
5+ year exp. in Data Modeling, Data Architecture, and Ontology/Semantic Modeling
Notes:
- The Semantic layer (Semantic modeling) piece is the key.
- That is the current gap they have from a skill set perspective.
- They have data modelers but none of them have that exp. so need someone who can come in and work on that as well as train others on it.
- Timbr (tool they are looking at moving to) could be HUGE if they have that exp. May be hard to find as it's a newer tool from a smaller company.
- Said within the Snowflake world that "Cortex" is very similar and could be an easy transition if they don't have Timbr.
- Implemented using OWL, RDF, and RDFS (need to have).
- The job that is needed: "How to ingest current data models into semantic layer and expose to users and AI tools".
- Sees this going past next October (so getting extended).
Job Summary
We are seeking a Senior Ontology Data Modeler to help build our enterprise semantic layer — designing ontologies and knowledge graphs that turn business meaning into a shared, machine-readable model for BI, AI, and agentic use cases. This is a foundational role: you will translate business concepts, relationships, and rules — much of which lives today in dashboards, reports, ETL logic, and subject-matter experts' heads — into governed, reusable ontologies that serve as a single source of truth across the enterprise. The ideal candidate combines strong data-modeling fundamentals, hands-on semantic/ontology expertise, and insurance domain knowledge.
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 existing business logic — extracting definitions and rules embedded in reports, dashboards, ETL/stored procedures — and capture tacit knowledge through structured sessions with business SMEs.
● Apply a hybrid modeling approach — bottom-up from source schemas and top-down from business concepts — including refining and validating AI-assisted (auto-generated) ontology candidates.
● Map ontology concepts to physical data sources and validate model outputs against source-of-truth systems and existing reports to ensure fidelity.
● 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, and support consuming teams across BI, analytics, and AI/agent workflows.
● Support data governance, metadata management, data lineage, and data quality initiatives; help decide which definitions and rules are authoritative.
● Ensure alignment with enterprise architecture, industry standards, and data governance best practices.
Required Qualifications
● 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., SQL-ontology tools such as Timbr, or comparable) 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).
● Awareness of emerging semantic-model interchange standards (e.g., open semantic/metric exchange formats).
What Success Looks Like
● Within the first phase, deliver 2–3 validated domain ontologies into production that answer real business questions for a consuming team — built on a repeatable, governed modeling process.
● Establish modeling standards and a maintainable operating rhythm so ontologies stay accurate as the business evolves.
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