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Semantic Jobs (NOW HIRING)

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Semantic Markup Analyst

New York, NY · Remote

We're looking for a Senior Business Analyst to support semantic markup (i.e., evaluating and maintaining our markup standards--and focused in particular on changes we may need to support non-book ...

PSRTEK

Senior Semantic Web Engineer (Hybrid)

Chicago, IL · On-site

$126K - $166K/yr

Position:- Senior Semantic Web Engineer Location:- Chicago, IL (Hybrid) Contract WHAT YOU'LL DO: * Serve as a subject matter expert in semantic web development on our graph data team, contributing to ...

Cotality

Senior Product Manager (Semantic Platform)

Dallas, TX · On-site

$125K - $165K/yr

The Senior Product Manager (Semantic Platform) is responsible for defining and evolving an internal platform that enables Cotality to capture, catalog, and evaluate enterprise semantic context. This ...

RXinsider LTD.

$171 - $209/hr

The second is semantic intelligence: the canonical data model, semantic layer design, and the knowledge frameworks through which data becomes understandable and trustworthy to internal teams ...

Cotality

$107 - $130/hr

## Senior Product Manager (Semantic Platform)Applylocations: Irvine, CA: Dallas, TXtime type: Full timeposted on: Posted Todayjob requisition id: REQ19096At Cotality, we are driven by a single mission ...

Codeforce 360

Google Looker/Semantic Layer

Dallas, TX · On-site

$113K - $136K/yr

About the Job- We are looking for an experienced Google Looker/Semantic Layer to join one of our enterprise client engagements. The ideal candidate should have strong expertise in Cloud Data ...

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All JobsSemantic Jobs

semantic information

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$55.5K

$118.7K

$173.5K

How much do semantic jobs pay per year?

As of Sep 6, 2026, the average yearly pay for semantic 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 a semantic job?

A Semantic job typically involves working with meaning and context in language, data, or technology. It may include roles in natural language processing (NLP), knowledge representation, search engine optimization (SEO), or semantic web technologies. Professionals in this field develop algorithms, ontologies, and models to improve understanding and classification of information. These jobs are common in AI, data science, and digital marketing industries.

What are the key challenges faced by semantic engineers when implementing knowledge graphs in large organizations?

Semantic Engineers often encounter challenges related to integrating disparate data sources, ensuring data quality, and aligning ontologies across departments. In large organizations, there can be legacy systems and inconsistent data formats, making it difficult to create a unified semantic model. Additionally, Semantic Engineers must collaborate closely with data architects, subject matter experts, and software developers to ensure the knowledge graph accurately reflects the organization's information needs and remains scalable as requirements evolve.

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

To thrive as a Semantic Analyst, you need expertise in linguistics, natural language processing (NLP), data analysis, and a relevant degree such as linguistics, computer science, or information science. Familiarity with tools like Python, NLP libraries (e.g., NLTK, spaCy), and semantic annotation systems is typically required. Strong analytical thinking, attention to detail, and effective communication skills help you interpret complex language data and collaborate with technical teams. These competencies are vital to accurately extract, structure, and apply meaning from language data, driving insights and solutions in various industries.

What is the difference between Semantic vs Data Analyst?

AspectSemanticData Analyst
Required CredentialsBackground in linguistics, computer science, or related fields; knowledge of semantic web technologiesDegree in statistics, mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentResearch-focused, often in tech or AI companies, working on language understandingBusiness or research settings, analyzing data to inform decisions
Industry UsageUsed in AI, NLP, and semantic web projectsUsed across finance, marketing, healthcare, and other sectors
Common Search/ComparisonSemantic vs Data Analyst

Semantic professionals focus on understanding and structuring meaning in language and data, often working with AI and NLP technologies. Data Analysts interpret data sets to generate insights for business decisions. While both roles involve data, Semantic roles emphasize language and knowledge representation, whereas Data Analysts focus on statistical analysis and reporting.

More about semantic jobs

What cities are hiring for Semantic jobs?

Cities with the most Semantic job openings:

What are the most commonly searched types of Semantic jobs?

The most popular types of Semantic jobs are:

What states have the most Semantic jobs?

States with the most job openings for Semantic jobs include:

What job categories do people searching Semantic jobs look for?

The top searched job categories for Semantic jobs are:

semantic jobs near you
Infographic showing various Semantic job openings in the United States as of August 2026, with employment types broken down into 90% Full Time, 5% Part Time, and 5% Contract. Highlights an 73% Physical, 6% Hybrid, and 21% Remote job distribution, with an average salary of $118,674 per year, or $57.1 per hour.

Senior Consultant - Semantic Data & AI Engineer

TheStaffed

New York, NY • On-site

$134K - $176K/yr

Contractor

Posted 18 days ago

Job description

Our client is seeking a Senior Consultant - Semantic Data & AI Engineer with 8-12 years of experience to design and deploy knowledge graphs, semantic layers, and AI-driven data solutions. This role combines deep expertise in graph technologies and semantic standards with practical application of modern machine learning and cloud platforms.
Responsibilities & Qualifications
  • Design and build knowledge graphs, semantic layers, ontologies, taxonomies, and graph-based data products; translate business concepts into machine-readable semantic models
  • Develop end-to-end data pipelines for acquiring, transforming, mapping, validating, and loading information; implement data-quality controls using SHACL validation
  • Integrate knowledge graphs with generative AI, RAG/GraphRAG, semantic search, and LLM applications; support NLP and document-intelligence use cases
  • Develop Python or Java-based data transformations, APIs, and services; create automated tests and support CI/CD pipelines and production deployments
  • Lead technical workstreams, mentor junior consultants, and facilitate requirements and modeling sessions with stakeholders
  • Participate in graph-platform evaluations and production deployments; produce technical designs, semantic models, and comprehensive documentation
  • Build vector search and semantic search capabilities; demonstrate proficiency in cloud platforms and containerized deployments

Requirements
  • 8-12 years of professional experience in data engineering, semantic technologies, or related fields
  • Expert-level knowledge of semantic standards and languages: RDF, RDFS, OWL, SPARQL, SHACL, SKOS, JSON-LD, and Turtle
  • Hands-on experience with graph databases and platforms: Neo4j, Stardog, GraphDB, Amazon Neptune, Anzo, MarkLogic, TigerGraph, Apache Jena, or TypeDB
  • Strong programming skills in Python and Java; proficiency with Git, CI/CD, automated testing, and containerization
  • Demonstrated expertise in data pipelines, NLP, machine learning, and vector search technologies
  • Experience with cloud platforms: AWS, Azure, Google Cloud, or tools such as Databricks, Snowflake, BigQuery, Redshift, or Microsoft Fabric
  • Agile methodology experience and a proven track record of delivering technical solutions in collaborative environments

Apply