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Data Governance Analyst Jobs in Michigan (NOW HIRING)

Works within IT Project Governance to provide oversight, direction and guidance/consultation for ... data and tools. Provide mentoring, coaching, training and on-boarding for project managers in the ...

Sr Data Management Analyst

Midland, MI · On-site

$73K - $93K/yr

The Senior Data Analyst leads initiatives for stringent data governance, collaborating cross-functionally to standardize data collection methodologies and ensure compliance. Facilitating end-user ...

Contribute to best practices for analytics engineering, data governance, and reporting standards. Required Qualifications * Strong proficiency in SQL for data transformation, analysis, and reporting.

Business Data Architect

Warren, MI · On-site

$60 - $77/hr

Professional certifications in data governance, data management, or analytics such as DAMA or CDMP are a plus. * Background in regulated, complex, or global environments with multiple business units ...

Implement Master Data Management (MDM), data quality, and data governance best practices. * Collaborate with architects, DBAs, analysts, business users, and technical teams to deliver scalable data ...

... data quality, governance, and integration initiatives. This role requires strong analytical capabilities, advanced ETL engineering skills, and the ability to work effectively across complex ...

New

This role will collaborate closely with product owners, analysts, and business stakeholders to ... Strong understanding of data architecture principles, data modeling techniques, and data governance ...

Showing results 21-40

Data Governance Analyst information

See Michigan salary details

$18

$47

$75

How much do data governance analyst jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for data governance analyst in Michigan is $47.74, according to ZipRecruiter salary data. Most workers in this role earn between $35.38 and $58.46 per hour, depending on experience, location, and employer.

Is data governance a good career?

Data governance is a growing field that involves establishing policies and standards to manage data quality, security, and compliance. It offers opportunities for roles such as Data Governance Analyst, requiring skills in data management tools and understanding regulatory requirements, making it a stable and in-demand career choice.

What is a data governance analyst?

A data governance analyst helps an organization maintain best practices regarding information security, integrity, and access. In this role, your duties are to come up with the appropriate protocols for keeping data, develop tools and methods for a business to oversee its data, and provide management with recommendations about how to best govern the use of and access to the information. Qualifications for this career include a bachelor’s degree in computer science or information systems, job experience with shared data and database languages such as SQL, skills such as analytical problem-solving, and a strong commitment to the ethical use of data.

How does a data governance analyst typically collaborate with other departments to ensure data quality and compliance?

A Data Governance Analyst regularly works with teams such as IT, legal, compliance, and business units to develop and enforce data management policies. This collaboration often involves coordinating data quality assessments, facilitating data stewardship programs, and ensuring that data handling adheres to relevant regulations and organizational standards. Analysts may lead cross-functional meetings, provide training on data governance best practices, and act as a liaison to resolve data-related issues. Effective communication and relationship-building are key to ensuring that all stakeholders are aligned on data quality and compliance goals.

What is the difference between Data Governance Analyst vs Data Quality Analyst?

AspectData Governance AnalystData Quality Analyst
CertificationsCDMP, DAMA certifications often preferredCDMP, Six Sigma, or data quality certifications common
Work EnvironmentFocus on policies, compliance, and data standardsFocus on data accuracy, validation, and issue resolution
Industry UsageUsed across finance, healthcare, and tech sectorsCommon in finance, retail, and healthcare industries
Search & Comparison IntentUnderstanding governance roles and responsibilitiesFocusing on data accuracy and quality improvement

While both roles involve working with data, a Data Governance Analyst primarily manages data policies, standards, and compliance, ensuring data is properly governed across the organization. In contrast, a Data Quality Analyst concentrates on assessing and improving the accuracy and integrity of data. Both roles often require similar certifications and are vital in data-driven industries, but they focus on different aspects of data management.

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

To thrive as a Data Governance Analyst, you need a solid understanding of data management principles, data quality frameworks, and regulatory compliance, often supported by a bachelor's degree in information systems, computer science, or a related field. Familiarity with data governance tools (like Collibra or Informatica), data cataloging systems, and knowledge of relevant data privacy regulations (such as GDPR or CCPA) are typically required. Strong analytical thinking, attention to detail, and effective communication skills help you collaborate across departments and enforce data policies. These abilities are crucial for ensuring data integrity, minimizing risk, and supporting informed decision-making within organizations.

What is a data governance analyst?

A Data Governance Analyst is a professional responsible for developing, implementing, and maintaining policies and procedures that ensure the accuracy, security, and proper usage of data within an organization. They work to ensure compliance with data-related regulations and standards, facilitate data quality initiatives, and help stakeholders understand their roles in data management. Data Governance Analysts often collaborate with IT, compliance, and business teams to establish data ownership, lineage, and stewardship. Their work is crucial in supporting data-driven decision-making and minimizing risks associated with data misuse.
What are the most commonly searched types of Data Governance Analyst jobs in Michigan? The most popular types of Data Governance Analyst jobs in Michigan are:
What job categories do people searching Data Governance Analyst jobs in Michigan look for? The top searched job categories for Data Governance Analyst jobs in Michigan are:
What cities in Michigan are hiring for Data Governance Analyst jobs? Cities in Michigan with the most Data Governance Analyst job openings:
What are popular job titles related to Data Governance Analyst jobs in MI? For Data Governance Analyst jobs in MI, the most frequently searched job titles are:
Infographic showing various Data Governance Analyst job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 12% Part Time, 1% Temporary, and 5% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $99,309 per year, or $47.7 per hour.

Sr IT Director- Data Analytics & AI, AFM, Commercial, Engineering & EV

Dana Incorporated

Novi, MI • On-site

Full-time

Re-posted 25 days ago


Dana Incorporated rating

5.8

Company rating: 5.8 out of 10

Based on 78 frontline employees who took The Breakroom Quiz

455th of 488 rated machine equipment manufacturers


Job description

Job Purpose

The Sr IT Director- Data Analytics & AI, AFM, Commercial, Engineering & EV is a senior leadership role responsible for defining and executing the company’s enterprise-wide data strategy, with a strong focus on Master Data Management (MDM), data governance, and AI-driven transformation across Aftermarket (AFM) and Commercial domains.

This role leads the end-to-end data value chain—from master data integrity and governance to advanced analytics and AI—ensuring that enterprise data is trusted, unified, and actionable. The Sr. Director will partner closely with business, digital, and engineering leaders to embed data and AI into core commercial and operational processes, driving measurable outcomes in revenue growth, customer experience, and operational performance.

Job Duties and Responsibilities

Enterprise Data & AI Strategy Leadership
•    Define and lead the enterprise data strategy, anchored in MDM, data governance, analytics, and AI, aligned to AFM and Commercial growth priorities.
•    Establish a multi-year roadmap spanning master data, data platforms, analytics, and AI/GenAI capabilities.
•    Act as a strategic advisor to executive leadership, shaping how data and AI drive competitive advantage, revenue, and operational excellence.
•    Build and lead a high-performing global organization across MDM, data engineering, governance, analytics, and data science.
Master Data Management (MDM) & Data Governance
•    Own and institutionalize enterprise MDM strategy and platforms across core domains (Customer, Product, Supplier, Pricing, Assets).
•    Establish data ownership, stewardship models, and domain accountability across AFM and Commercial.
•    Drive data standardization, harmonization, and lifecycle management to enable consistent reporting and AI readiness.
•    Lead enterprise-wide data governance frameworks, including policies, quality management, lineage, and metadata.
•    Ensure compliance with regulatory, privacy, cybersecurity, and intellectual property standards.
•    Define and track data quality KPIs and drive continuous improvement across business domains.
Data Platforms & Architecture
•    Own the strategy and evolution of modern data platforms, including lakehouse architectures, real-time data pipelines, and semantic data layers.
•    Ensure platforms are AI-ready, scalable, secure, and optimized for cost and performance.
•    Partner with Enterprise Architecture and Cybersecurity to enforce standards, data models, and integration patterns.
•    Enable seamless integration of ERP, CRM, supply chain, and engineering data into unified data products.
Analytics, AI & Advanced Capabilities
•    Define and scale a portfolio of high-impact analytics and AI use cases, including: 
o    Commercial performance, pricing, and margin optimization
o    Aftermarket demand forecasting and parts optimization
o    Customer insights and segmentation
o    Predictive maintenance and service optimization
o    AI-enabled anomaly detection and operational intelligence
o    Generative AI for commercial insights, automation, and decision support
•    Lead the end-to-end AI lifecycle (ideation to production) with strong MLOps and governance practices.
•    Establish a product-based data & analytics operating model, delivering reusable, scalable data products and AI capabilities.
AFM & Commercial Business Alignment
•    Partner with AFM and Commercial leaders to translate business strategy into data, MDM, and AI solutions.
•    Ensure master and transactional data enable core commercial processes including quoting, pricing, forecasting, and customer engagement.
•    Drive use of data and AI to enhance revenue growth, profitability, and customer experience.
•    Act as the primary data and AI leader for AFM and Commercial transformation initiatives.

Education and Qualifications

Required
•    Bachelor’s degree in Computer Science, Engineering, Data, or related field (Master’s preferred).
•    12–15+ years of experience in enterprise data, MDM, analytics, and AI leadership roles.
•    Proven track record leading enterprise-scale MDM and data transformation programs.
•    Deep expertise in data governance, master data domains, and modern data architectures.
•    Experience delivering AI/analytics solutions with measurable commercial impact.
•    Strong leadership experience managing global, cross-functional teams and transformation programs.
Preferred
•    Hands-on experience with MDM tools/platforms, data quality frameworks, and metadata management.
•    Familiarity with AI/ML, MLOps, and GenAI applications in commercial or industrial settings.
•    Experience in manufacturing, aftermarket, or asset-intensive industries.
•    Exposure to OT/IT convergence and engineering data ecosystems.
•    Strong executive presence with ability to influence at C-suite level.
Measures of Success
•    Business value delivered through data, MDM, and AI initiatives (revenue, margin, cost, productivity)
•    Enterprise data quality, consistency, and governance maturity
•    Adoption and impact of analytics and AI in AFM and Commercial operations
•    Speed and scalability of data product and AI delivery
•    Effectiveness of MDM in enabling enterprise-wide insights and processes


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