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Semantic Modeling Jobs (NOW HIRING)

CAPTRUST

Principal Semantic Engineer

Raleigh, NC

Promote best practices for semantic modeling, knowledge representation, and AI governance through documentation, patterns, and enablement. Minimum Qualifications: * Bachelor's degree in data ...

CAPTRUST

Principal Semantic Engineer

Raleigh, NC · On-site

$131K - $175K/yr

Promote best practices for semantic modeling, knowledge representation, and AI governance through documentation, patterns, and enablement. Qualifications Minimum Qualifications: * Bachelor's degree ...

Orion180

Data Modeling Lead

Irving, TX · On-site

Orion180 is seeking a Semantic Model Manager to own the suite of semantic models that drive analytics across underwriting, claims, finance, sales, and executive reporting. You will be the steward of ...

TetraScience

Data & Semantic Model Architect

$65.25 - $84/hr

The "Trees" - Hands-on Modeling: Roll up your sleeves to design and implement complex ontologies ... Semantic Fluency: Deep, hands-on expertise with semantic web standards (RDF, OWL, SHACL, SPARQL ...

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Semantic Modeling information

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$55.5K

$118.7K

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How much do semantic modeling jobs pay per year?

As of Jun 7, 2026, the average yearly pay for semantic modeling in the United States is $118,674.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,500.00 and $133,500.00 per year, depending on experience, location, and employer.

What is the difference between Semantic Modeling vs Data Modeling?

AspectSemantic ModelingData Modeling
PurposeFocuses on capturing meaning and relationships within data to improve understanding and interoperabilityDefines how data is structured, stored, and accessed in databases
CredentialsOften requires knowledge of ontologies, knowledge representation, and sometimes domain-specific expertiseTypically requires understanding of database design, normalization, and data architecture
Work EnvironmentUsed in knowledge graphs, AI, and semantic web projectsUsed in relational, NoSQL, and data warehouse environments
Industry UsageCommon in AI, semantic web, and information integration projectsCommon in software development, database administration, and data engineering

Semantic Modeling and Data Modeling are related but serve different purposes. Semantic Modeling emphasizes understanding and representing the meaning of data, often used in AI and knowledge systems. Data Modeling focuses on structuring data efficiently within databases. Both are essential for effective data management but are applied in different contexts.

What are the key skills and qualifications needed to thrive as a Semantic Modeler, and why are they important?

To thrive as a Semantic Modeler, you need a strong background in data modeling, ontology design, and domain-specific knowledge, often supported by a degree in computer science or information science. Familiarity with semantic web technologies such as RDF, OWL, SPARQL, and tools like Protégé is typically required, along with experience in database management systems. Analytical thinking, attention to detail, and effective communication are essential soft skills for collaborating with stakeholders and translating complex requirements into structured models. These skills ensure the creation of accurate, reusable, and scalable semantic models that support data interoperability and meaningful information retrieval.

What is semantic modeling?

Semantic modeling is the process of creating structured representations of data that capture the meaning, relationships, and context of information within a specific domain. It involves defining entities, attributes, and the connections between them, often using ontologies or conceptual models. Semantic modeling is widely used in areas like knowledge graphs, data integration, and artificial intelligence to ensure that systems can interpret and use data accurately. By providing a common understanding of data, semantic models enable better interoperability, data quality, and decision-making across different applications.

What are some common challenges faced by professionals working in Semantic Modeling, and how can they be addressed?

Professionals in Semantic Modeling often encounter challenges such as aligning diverse data sources, ensuring consistency in ontologies, and effectively communicating complex models to non-technical stakeholders. Addressing these challenges typically involves collaborating closely with domain experts, using standardized vocabularies, and leveraging tools that support visualization and validation of semantic structures. Regular team reviews and iterative refinement of models can also help ensure accuracy and usability, making collaboration and adaptability key aspects of success in this field.
Infographic showing various Semantic Modeling job openings in the United States as of May 2026, with employment types broken down into 4% Internship, 92% Full Time, and 4% Contract. Highlights an 59% In-person, 8% Hybrid, and 33% Remote job distribution, with an average salary of $118,674 per year, or $57.1 per hour.

Semantic Modeler

Tata Consultancy Service Limited

Seattle, WA • On-site

$100K - $160K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 25 days ago

Job description

Must Have Technical/Functional Skills
Seeking a functional semantic modeler to
• Define and implement a centralized metric store and semantic layer that will help develop business requirements and then translate business requirements into repeatable, governed metric definitions and production-ready models
• The role combines the technical know-how on data modeling, semantic tooling (dbt Metricflow / dbt semantic layer or equivalent), knowledge graph concepts, BI/semantic mapping to deliver reliable metrics and data-driven insights at scale using a centralized metric store
• Hands-on experience with dbt Metricflow and/or dbt Semantic Layer (designing and implementing metric catalogs and dbt models)
Roles & Responsibilities
• Define and implement a centralized metric store and semantic layer
• Translate business requirements into repeatable, governed metric definitions and production-ready models
• Design and implement metric catalogs and dbt models using dbt metricflow and/or dbt semantic layer
• Deliver reliable metrics and data-driven insights at scale using a centralized metric store
• Technical know-how on data modeling, semantic tooling (dbt Metricflow / dbt semantic layer or equivalent), knowledge graph concepts, BI/semantic mapping
Base Salary Range : $100,000 to $160,000 Per Annum
TCS Employee Benefits Summary:
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
Family Support: Maternal & Parental Leaves.
Insurance Options: Auto & Home Insurance, Identity Theft Protection.
Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
Time Off: Vacation, Time Off, Sick Leave & Holidays.
Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.
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