Senior Ontology Data Modeler

ClifyX; West Des Moines (IA); On-site; USD 120,000 - 180,000; Full time

Job summary

ClifyX is seeking a Senior Data Architect specialized in data modeling, ontology development, and semantic technologies to shape enterprise knowledge graphs. You will design scalable data models and collaborate with business stakeholders to translate requirements into semantic solutions.

The role emphasizes hands-on work with RDF/OWL, AWS data platforms, and governance practices, with an eye for aligning data architecture to enterprise standards and enabling BI and AI initiatives.

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.
• Proficiency with SQL and experience modeling on cloud data platforms (AWS), plus familiarity with S3/Apache Iceberg.
• Insurance domain experience (Annuity preferred).
• Experience translating business requirements into scalable data and semantic solutions with business stakeholders.
• Strong analytical, communication, and stakeholder-management skills.

Responsibilities

• Design, build, and maintain enterprise ontologies and knowledge graphs.
• Define business entities, relationships, and semantic rules across data domains.
• Model insurance domains: Policy, Claims, Underwriting, Customer, Product, Sales.
• Harvest insights from reports and ETL pipelines; capture tacit knowledge with business SMEs.
• Apply hybrid modeling: bottom-up sources and top-down concepts; refine with AI-assisted candidates.
• Map ontology concepts to data sources and validate outputs against source-of-truth systems.
• Version, test, and promote ontology artifacts through DEV–QA–STAGE–PROD with Git.
• Collaborate with Data Architects/Engineers and business SMEs across BI and AI workstreams.
• Support governance, metadata, data lineage, and data quality initiatives.
• Ensure alignment with enterprise architecture and governance standards.

Skills

Data modeling

Data architecture

Ontology/semantic modeling

Stakeholder management

Communication skills

Analytical skills

Bridge business and technical teams

Tools

SQL

AWS

RDF/RDFS/OWL

S3

Apache Iceberg

Knowledge graph design

Tableau

Power BI

Job description

• 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)
• 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 initiative
• Ensure alignment with enterprise architecture, industry standards, and data governance best practices