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Semantic Modeling Jobs (NOW HIRING)

Prophecy Technologies

Ontology Consultant

We are seeking an experienced Ontology Business Consultant to design and evolve privacy-focused semantic models that capture complex business concepts, relationships, and constraints. The role ...

Kiongroup

Senior AI Context Engineer

Atlanta, GA · Hybrid

$134K - $179K/yr

This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data engineering, semantic modeling, and AI-oriented data platform design. The ideal candidate combines ...

Kion Group AG

Senior AI Context Engineer

Atlanta, GA · On-site

$134K - $179K/yr

This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data engineering, semantic modeling, and AI-oriented data platform design. The ideal candidate combines ...

Kiongroup

Senior AI Context Engineer

Wauwatosa, WI · Hybrid

$134K - $179K/yr

This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data engineering, semantic modeling, and AI-oriented data platform design. The ideal candidate combines ...

Kiongroup

Senior AI Context Engineer

Atlanta, GA · Hybrid

$134K - $179K/yr

This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data engineering, semantic modeling, and AI-oriented data platform design. The ideal candidate combines ...

Kiongroup

Senior AI Context Engineer

Grand Rapids, MI · Hybrid

$134K - $179K/yr

This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data engineering, semantic modeling, and AI-oriented data platform design. The ideal candidate combines ...

Kiongroup

Senior AI Context Engineer

Grand Rapids, MI · Hybrid

$134K - $179K/yr

This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data engineering, semantic modeling, and AI-oriented data platform design. The ideal candidate combines ...

Kiongroup

Senior AI Context Engineer

Wauwatosa, WI · Hybrid

$134K - $179K/yr

This is a hands-on senior/principal-level engineering role requiring deep expertise in cloud data engineering, semantic modeling, and AI-oriented data platform design. The ideal candidate combines ...

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Semantic Modeling information

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$55.5K

$118.7K

$173.5K

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.
Ontology / Knowledge Engineer Lead - Chase Semantic Layer

Ontology / Knowledge Engineer Lead - Chase Semantic Layer

JPMorgan Chase & Co

Jersey City, NJ • On-site, Remote

Full-time

Medical, Retirement

Posted 2 days ago

JPMorgan Chase & Co. rating

8.1

Company rating: 8.1 out of 10

Based on 468 frontline employees who took The Breakroom Quiz

46th of 141 rated banks

Job description

As an Ontology and Knowledge Graph Engineer in Chase's Data and Analytics Office, you will curate the semantic data assets that connect our enterprise data estate to a shared, intelligent knowledge graph. You will work at the intersection of formal knowledge representation, logical data modeling, and data integration, building the ontologies and mapping assets that make our data semantically interoperable across use cases. Our team values precision, intellectual curiosity, and a deep commitment to making data meaningful - and you will find that culture reflected in everything we build together. This role offers an opportunity to contribute to a foundational capability that underpins enterprise AI, analytics, and data governance at one of the world's most influential financial institutions. 

Job Responsibilities 

  • Author Logical Data Model Ontologies that compose concepts from Upper Ontologies and Semantic Taxonomies to accurately represent how enterprise data is materialized across our data estate 

  • Design and maintain Knowledge Graph Mapping assets that connect relational databases, REST APIs, in-memory data structures, and real-time streaming sources to a coherent enterprise knowledge graph 

  • Curate Semantic Taxonomy structures using controlled vocabularies and Concept Schemes to organize enterprise concepts consistently across multiple business domains 

  • Contribute to the design and governance of Upper Ontologies and Semantic Taxonomies that provide a shared, standardized conceptual backbone across enterprise semantic use cases 

  • Enable Virtual Knowledge Graph capabilities by ensuring mapping assets and ontology definitions support on-the-fly knowledge graph materialization without physical data movement 

  • Engage with data architects, domain subject matter experts, AI engineers, and machine learning engineers to align ontology and mapping design decisions with both physical data structures and downstream Reasoning and Semantic Validation requirements 

  • Participate in ontology governance activities including versioning, change management, deprecation policies, and cross-domain alignment reviews 

  • Translate complex business and data requirements into formal semantic representations that are technically rigorous and accessible to non-technical stakeholders 

Required Qualifications, Capabilities, and Skills 

  • 3 years of experience working with semantic web technologies, knowledge graph engineering, ontology development, or linked data systems in a professional or research setting 

  • Demonstrated understanding of formal knowledge representation principles, including class hierarchies, property definitions, and logical constraints 

  • Familiarity with data mapping concepts that connect structured and semi-structured data sources to ontology-defined target vocabularies 

  • Working knowledge of semantic data model layers, including foundational data models, schema definition languages, and controlled vocabulary organization standards 

  • Exposure to relational databases and semi-structured data sources, including REST APIs, in-memory structures, and streaming data pipelines 

  • Ability to translate business and data requirements into formal semantic models in collaboration with data architects, domain experts, and engineering teams 

  • Awareness of Virtual Knowledge Graph concepts and the principles of connecting heterogeneous data sources to a shared semantic layer without physical data movement 

Preferred Qualifications, Capabilities, and Skills 

  • Hands-on experience authoring ontologies and knowledge graph mapping assets in a production enterprise environment 

  • Experience contributing to enterprise-scale knowledge graph programs within a large, complex organization 

  • Familiarity with Reasoning and Semantic Validation frameworks used to enforce syntactic and semantic correctness of ontology-defined concepts 

  • Exposure to Upper Ontology design patterns and their role in standardizing conceptual overlaps across multiple enterprise use cases 

  • Experience communicating complex semantic modeling decisions to non-technical stakeholders, including business analysts and product owners 

Chase is a leading financial services firm, helping nearly half of America's households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs. 

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions.  We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. 

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Equal Opportunity Employer/Disability/Veterans

Our Consumer & Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We're proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions - all while ranking first in customer satisfaction.

The CCB Data & Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.

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