Data Governance Analyst

Data Governance Analyst (hybrid)
Salt Lake City (hybrid)
Direct Hire
Pay: $100k - $115k

Job Description:

About the Role
Our client is seeking a Data Governance Analyst to help prepare organizational data for AI and advanced analytics. This role focuses on building a modern governance program, improving data quality at the source, and partnering across business and technical teams. You will report to the VP of Data and Business Intelligence and play a key role in establishing scalable, high-quality data practices.

What You'll Do

• Establish and manage data governance policies, standards, and processes
• Build governance frameworks and actionable roadmaps
• Define key data elements and quality rules; monitor and remediate issues
• Partner with system owners to improve upstream data quality and capture
• Maintain a centralized business glossary and metadata
• Use SQL for data profiling, analysis, and anomaly detection
• Ensure compliance with privacy, security, and retention requirements; support audits
• Drive governance adoption through training and change management
• Set AI data readiness standards and guide teams toward compliance
• Report on governance metrics, risks, and progress
• Collaborate cross-functionally to meet regulatory and security requirements

• Bachelor's degree or equivalent experience in a relevant field
• 3-6+ years in data governance, data quality, data management, or related roles
• Strong SQL skills for data analysis and root-cause identification
• Understanding of data modeling (normalization/denormalization)
• Experience implementing data quality rules, metrics, and workflows
• Familiarity with governance frameworks (e.g., DAMA-DMBOK)
• Experience with metadata management and business glossaries
• Ability to improve data capture at the source (validations, reference/master data)
• Strong communication and cross-functional collaboration skills

• Experience with BI tools (e.g., Power BI, Tableau)
• Familiarity with data catalogs (e.g., Purview, Collibra, Alation)
• Exposure to MDM, reference data management, and AI/ML readiness

• Established governance framework with clear policies, roles, and roadmap
• Improved data quality and compliance across systems
• Embedded governance practices through training and adoption
• Defined and implemented AI data readiness standards
• Effective cross-functional alignment on regulatory and data governance priorities

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