Data Modeler

• Role: Semantic Data Modeler with AI and Ontology Expertise
• Location: Dallas, TX – Hybrid Role
• Full-time Role, only visa independent candidate

We are seeking an experienced Semantic Data Modeler with strong AI, ontology, and knowledge graph expertise to design and govern enterprise semantic models that make data consistent, interoperable, and AI-ready. This role will bridge traditional data modeling, semantic-layer design, ontology engineering, and Gen-AI-enabled analytics by translating complex business concepts into governed semantic structures that support BI, self-service analytics, semantic search, knowledge graphs, and natural language query experiences.

• Experience: 8+ years overall IT/data experience, including 5+ years in data modeling and semantic model development; 2+ years preferred in ontology, knowledge graph, or AI-enabled data products
• Employment Type: Full-time

Key Responsibilities:

• Design, develop, and govern enterprise semantic data models that define business entities, attributes, relationships, hierarchies, metrics, dimensions, and KPIs.
• Translate business requirements into conceptual, logical, physical, and semantic model designs that align with enterprise data architecture and governance standards.
• Develop ontology-driven semantic structures, including taxonomies, controlled vocabularies, canonical concepts, relationship types, constraints, and reusable business definitions.
• Design and maintain knowledge graph-ready models that support semantic interoperability, entity resolution, relationship-aware analytics, semantic search, reasoning, and AI grounding.
• Map relational, dimensional, API, streaming, and Lakehouse data structures into governed semantic models and ontology concepts.
• Partner with business stakeholders, domain SMEs, data architects, data engineers, BI teams, AI/ML teams, and governance teams to resolve data-definition conflicts and validate model design.
• Support Gen-AI and natural language analytics use cases by enabling consistent business terminology, semantic grounding, metadata enrichment, and trusted data definitions.
• Establish ontology and semantic modeling governance practices, including versioning, naming standards, change management, lineage, data quality rules, and reuse guidelines.
• Document semantic assets, including entity definitions, relationship definitions, business rules, model mappings, assumptions, constraints, and data lineage.

• Required Skills and Qualifications
• 8+ years of experience in data architecture, data modeling, data warehousing, analytics, information architecture, or related data management roles.
• 5+ years of hands-on experience designing logical, physical, dimensional, relational, and semantic data models.
• Strong understanding of semantic modeling concepts, including business entities, dimensions, measures, hierarchies, canonical models, metadata, business glossaries, and semantic layers.
• Hands-on or working knowledge of ontology and knowledge representation concepts, including classes, properties, relationships, constraints, axioms, taxonomies, and controlled vocabulary.
• Experience or strong familiarity with semantic web and ontology standards such as RDF, RDFS, OWL, SKOS, SHACL, SPARQL, JSON-LD, or Turtle.
• Experience with knowledge graph concepts, graph data modeling, entity resolution, relationship modeling, graph query patterns, and semantic validation.
• Strong SQL skills with the ability to analyze, profile, validate, and reconcile data across multiple source systems.
• Experience with cloud-based data platforms such as Collabra, OneLake, Azure, SQL Server, Snowflake, Databricks, or equivalent modern data platforms.
• Ability to collaborate with AI, ML, data science, and analytics teams to support AI-ready data products, semantic grounding, and natural language query use cases.
• Strong communication and facilitation skills to translate complex business concepts into formal models that are clear to both technical and non-technical stakeholders.

• AI and Gen AI Skills
• Understanding of how semantic models, ontologies, and metadata improve AI/GenAI outcomes through grounding, context enrichment, explainability, and reduced ambiguity.
• Familiarity with Text-to-SQL, natural language BI, semantic search, retrieval-augmented generation, and AI-assisted analytics patterns.
• Ability to define AI-consumable business terms, entities, relationships, metrics, synonyms, and domain rules for trusted query and retrieval experiences.
• Experience supporting AI-ready data products by aligning source-system data, canonical models, metadata, lineage, and governed business definitions.
• Exposure to vector search, embeddings, LLM prompt grounding, knowledge graph-enhanced RAG, or graph-based context retrieval is preferred.
• Ability to partner with AI/ML engineers and data scientists to identify the semantic structures required for model features, reasoning, recommendations, and intelligent automation.

• Ontology and Knowledge Graph Skills
• Ability to design business ontologies that define enterprise concepts, concept hierarchies, relationships, constraints, and reusable domain vocabulary.
• Experience creating taxonomies, controlled vocabulary, canonical models, and concept schemes that standardize meaning across business and technical teams.
• Familiarity with RDF, OWL, SKOS, SHACL, SPARQL, RDFS, JSON-LD, Turtle, and linked-data principles.
• Experience mapping relational schemas, dimensional models, APIs, and Lakehouse tables into ontology concepts and knowledge graph structures.
• Knowledge of ontology governance practices such as versioning, change control, deprecation policies, stewardship, reuse standards, and cross-domain alignment reviews.
• Familiarity with ontology and graph tools such as Protégé, TopBraid, PoolParty, VocBench, Neo4j, Stardog, GraphDB, Amazon Neptune, or equivalent platforms is preferred.
• Ability to apply semantic validation rules and constraints to improve model quality, consistency, and interoperability.
• Awareness of industry reference ontologies and models such as FIBO, BIAN, GS1, TM Forum, OSI or other domain-specific standards is preferred.