Semantic Data Modeler with AI and Ontology Expertise

Semantic Data Modeler with AI and Ontology Expertise | Dallas, TX or Remote, USA | Fulltime (Permanent).

Role Summary:

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 GenAI-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 modelling and semantic model development

• 2+ years preferred in ontology, knowledge graph, or AI-enabled data products

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 GenAI 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 modelling, 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 modelling 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 GenAI 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 Protg, 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.