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Semantic Jobs in Michigan (NOW HIRING)

$96K - $130K/yr

Build and maintain Power BI semantic models and dashboards. * Own platform operations: monitoring, alerting, and data quality. * Establish and maintain data governance: shared definitions, quality ...

Senior Data Engineer

Clinton, MI · On-site

$120 - $150/hr

Build and maintain Power BI semantic models and dashboards. * Own platform operations: monitoring, alerting, and data quality. * Establish and maintain data governance: shared definitions, quality ...

Marketing Data Domain Lead

Novi, MI · On-site

$81K - $101K/yr

Own marketing data products, certified datasets, and semantic models that support trusted analytics and self-service reporting for Marketing and BI teams. * Establish and maintain governance ...

Enterprise Data Architect

Livonia, MI · On-site

$104 - $163/hr

Establish and evolve the semantic and dimensional modeling framework used by BI and Engineering.* Evaluate and lead implementation of master data management tools and processes to support the ...

Showing results 41-60

Semantic information

See Michigan salary details

$48.4K

$103.4K

$151.2K

How much do semantic jobs pay per year?

As of Sep 5, 2026, the average yearly pay for semantic in Michigan is $103,436.00, according to ZipRecruiter salary data. Most workers in this role earn between $85,000.00 and $116,400.00 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 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.

What are the most commonly searched types of Semantic jobs in Michigan?

The most popular types of Semantic jobs in Michigan are:

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

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

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

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

Infographic showing various Semantic job openings in Michigan as of August 2026, with employment types broken down into 81% Full Time, 15% Part Time, and 4% Contract. Highlights an 71% Physical, 6% Hybrid, and 23% Remote job distribution, with an average salary of $103,436 per year, or $49.7 per hour.

Microsoft Azure Architect -- Fabric

Reliable Software Resources

Detroit, MI • On-site

$58 - $75.75/hr

Other

Posted 18 days ago


Job description

Position: Microsoft Azure Architect -- Fabric

Detroit, MI

Longterm contract

Skills:

  • Design end-to-end Microsoft Fabric architecture.
  • Build solutions using OneLake, Lakehouse, Data Warehouse, Data Factory, Real-Time Intelligence, and Power BI.
  • Implement Medallion Architecture (Bronze, Silver, Gold) and Delta Lake best practices.
  • Develop scalable data ingestion, ETL/ELT, and orchestration pipelines using Fabric Data Factory and Spark.
  • Design semantic models, Direct Lake, dashboards, and reports in Power BI.
  • Define data governance, security, and lineage using Microsoft Purview and Microsoft Entra ID.
  • Optimize Fabric capacity, Spark workloads, Warehouse performance, and Power BI reports.
  • Implement CI/CD using Git integration and Fabric Deployment Pipelines.
  • Lead client workshops, architecture reviews, solution design, and technical delivery.
  • Mentor technical teams and ensure adherence to architecture and coding standards.