Semantic Data Modeler

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.