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Semantic Jobs in Texas (NOW HIRING)

Insperity

Senior Semantic Data Architect

Kingwood, TX · On-site

$112K - $152K/yr

We are seeking a talented Senior Semantic Data Architect to join our team SUMMARY: This position is responsible for anchoring the design, development, and governance of the enterprise semantic model ...

Insperity

Senior Semantic Data Architect

Houston, TX · On-site

$123K - $168K/yr

We are seeking a talented Senior Semantic Data Architect to join our team SUMMARY: This position is responsible for anchoring the design, development, and governance of the enterprise semantic model ...

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 ...

New

CSAA

Job Title Sr. Manager, AI Lead - Semantic Layer - Remote Requisition Number R7755 Sr. Manager, AI Lead - Semantic Layer - Remote (Open) Location California - Home Teleworkers Additional Locations ...

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Semantic information

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$51.7K

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$161.6K

How much do semantic jobs pay per year?

As of Aug 15, 2026, the average yearly pay for semantic in Texas is $110,563.00, according to ZipRecruiter salary data. Most workers in this role earn between $90,800.00 and $124,400.00 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 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.

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 are the most commonly searched types of Semantic jobs in Texas?

The most popular types of Semantic jobs in Texas are:

What are popular job titles related to Semantic jobs in Texas?

For Semantic jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Semantic jobs in Texas look for?

The top searched job categories for Semantic jobs in Texas are:

Semantic jobs near you
Infographic showing various Semantic job openings in Texas as of August 2026, with employment types broken down into 88% Full Time, 6% Part Time, and 6% Contract. Highlights an 72% Physical, 7% Hybrid, and 21% Remote job distribution, with an average salary of $110,563 per year, or $53.2 per hour.

Senior Semantic Data Architect

Insperity

Kingwood, TX • On-site

$112K - $152K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 16 days ago

Insperity rating

7.9

Company rating: 7.9 out of 10

Based on 33 frontline employees who took The Breakroom Quiz

133rd of 492 rated business services

Job description

Insperity provides the most comprehensive suite of scalable HR solutions available in the marketplace with an optimal blend of premium HR service and technology. With more than 90 locations throughout the U.S., Insperity is currently making a difference for thousands of businesses and communities nationwide.
Behind our success is the unshakeable belief in the value of our people. We value diversity, inclusivity and a sense of belonging. We celebrate work and life events, and we partner with our clients and communities to make great things happen.
We've earned recognition time and again as a top place to work-named among the best by respected organizations like Glassdoor and U.S. News & World Report. We're also proud to be recognized for one of the country's Top 50 Midsize Early Talent Programs through RippleMatch's Campus Forward Awards. There's never been a better time to be part of Insperity, and our best work is still ahead. Learn more at Insperity.com.
Why Insperity?
Flexibility: Over 80% of Insperity's jobs have flexibility. We want your time to have balance, whether it's spent with coworkers, clients, family or your community.
Career Growth: Insperity provides many ways to grow with the company. We offer continuous learning programs, mentorship opportunities and ongoing training.
Well-Being: Our total rewards package includes generous paid time off, top-tier medical, dental and vision benefits, health & wellness support, paid volunteer hours and much more. We take care of our people so that you can do your best work.
We are seeking a talented Senior Semantic Data Architect to join our team
SUMMARY:
This position is responsible for anchoring the design, development, and governance of the enterprise semantic model that underpins the Insperity AI Platform. It defines how business entities, relationships, and rules are consistently represented across domains, enabling reliable AI, analytics, and data products. The role partners with subject matter experts, AI Program Managers, Platform Architecture, and governance bodies to translate business definitions into a unified semantic layer. It also ensures ongoing alignment, adoption, and integrity of the model as business processes and platform capabilities evolve.
RESPONSIBILITIES:
  • Designs and maintains the enterprise semantic model (ontology, taxonomy, and controlled vocabularies) across HR, Payroll, Benefits, Risk, and Service Operations domains, ensuring consistent representation of core business entities and relationships across the Insperity AI portfolio.
  • Elicits authoritative business definitions from subject matter experts and resolves cross-domain definitional conflicts, codifying the result into a unified semantic layer used by AI agents, analytics, and downstream data products.
  • Defines and maintains data product standards covering schema conventions, naming, definitional consistency, and lineage; partners with engineering and data leadership to drive adoption across business units and fusion teams.
  • Partners with Platform Architects and Enterprise Data Engineering to integrate the semantic model into the Insperity AI Platform's grounding layer, ensuring agents draw on consistent business definitions at inference time and that semantic changes propagate cleanly into production.
  • Partners with AI Program Managers across business units to translate domain definitions into the unified ontology and to keep the model aligned as business processes evolve.
  • Receives knowledge transfer from the external semantic layer consultants and assumes long-term ownership of the model post-handoff, ensuring continuity, ongoing evolution, and institutional capability beyond the initial build.
  • Governs the change control process for the semantic model, reviewing additions, deprecations, and modifications against impact on downstream consumers; partners with engineering and data leadership on release coordination.
  • Documents the semantic model in forms usable by both technical (engineering, data) and business (operations, compliance) audiences, including conceptual diagrams, definitional references, and lineage views.
  • Partners with Legal, Compliance, and the AI Governance Board to align definitional decisions with policy, regulatory requirements, and responsible AI principles, particularly where semantic choices intersect with regulated data domains.
  • Maintains traceability between business definitions, data product schemas, and downstream consumers (AI agents, analytics, reporting), enabling impact analysis and confident change management as the platform scales.
  • Champions definitional rigor across the AI Platform program, building shared understanding of why consistent business representation is foundational to trustworthy AI and engaging stakeholders to reinforce that discipline.

QUALIFICATIONS:
  • Bachelor's Degree or higher in Information Science, Computer Science, Library Science, Knowledge Management, Linguistics, or related field is required.
  • Five to seven years of proven experience in semantic modeling, ontology engineering, taxonomy design, or enterprise data architecture is required.
  • Three years of experience working with the data foundations of AI applications, including semantic layers, knowledge graphs, or retrieval augmented generation grounding, is preferred.
  • Demonstrated experience receiving and operationalizing knowledge transfer from an external consulting engagement is preferred.
  • Demonstrated experience designing and maintaining a semantic model consumed by multiple downstream systems, including AI applications, analytics platforms, and operational data products.
  • Working knowledge of at least one formal semantic modeling approach, such as OWL/RDF, Object Role Modeling, UML conceptual modeling, or relational semantic layer platforms (e.g., Cube, dbt, AtScale).
  • Strong skill at eliciting definitions from non-technical subject matter experts and translating them into precise, machine-usable models.
  • Proven ability to facilitate cross-functional definitional disputes to consensus, including where stakeholders disagree on whether a term means the same thing across business contexts.
  • Excellent communication, collaboration, and problem-solving skills, with the ability to translate between technical and business audiences.
  • Ability to think strategically and creatively and adapt to changing needs and priorities.
  • Proficiency in documentation tools and modeling environments (e.g., Protege, PoolParty, ontology IDEs, or equivalent semantic-layer authoring tools).
  • Demonstrated leadership in data foundations for AI, including data governance, cataloging, lineage, quality standards, metadata management, and labeling workflows.
  • Familiarity with graph databases (e.g., Neo4j, GraphDB, Amazon Neptune) or knowledge graph platforms.
  • Familiarity with data governance frameworks (e.g., DAMA-DMBOK, DCAM) and data product practices.
  • Strong proficiency in retrieval-based AI grounding, including hybrid RAG, citations, and indexing/chunking strategies as they intersect with the semantic layer.
  • Domain experience in HR, Payroll, Benefits, or Professional Employer Organization (PEO) services is preferred.
  • Working knowledge of security, compliance, and responsible AI principles, including PII handling, data minimization, and lineage requirements for regulated domains.

This job specification should not be construed to imply that these requirements are the exclusive standards of the position. Incumbent will follow any other instructions, and perform any other related duties, as may be required by the supervisor.
At Insperity, we celebrate the diversity of our employees and our leadership. Insperity is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status or any other characteristic protected by law.

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About Insperity

Sourced by ZipRecruiter

Take care of your people Insperity has a long history of improving the success equation of small and midsize businesses across the country – because when businesses succeed, communities prosper. And in today’s changing business environment, it’s our privilege to take care of an organization’s most valuable asset: its people.

Company size

1,001 - 5,000 Employees

Headquarters location

Houston, TX, US

Year founded

1986

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