Data Governance Analyst - Data Quality & Metadata

Dow Inc.; Stafford (TX); On-site; USD 65,000 - 110,000; Full time

Benefits offered by this job

Bonus opportunity

401(k) matching

Employee discounts

Paid time off

Job summary

Dow seeks a Data Governance Analyst to support enterprise governance practices. Located in Midland, MI or Houston, TX, the role focuses on governance artifacts, metadata, data quality, and stewardship across business domains.

The successful candidate partners with business and technical teams to improve usability of enterprise data assets, ensuring governance standards are consistently applied across domains.

Qualifications

• Bachelor’s degree or equivalent in data governance or related field.
• At least 2 years in data governance, data management, analytics, or related area.
• Foundational understanding of metadata and data quality concepts.
• Experience partnering with business and technical stakeholders.

Responsibilities

• Execute established data governance processes, policies and controls.
• Create and maintain business/technical metadata and data glossary entries.
• Document data ownership, stewardship, and data lineage.
• Maintain governance repositories and standards documentation.
• Perform data quality assessments and monitor metrics.
• Collaborate with Data Governance Managers and SMEs to capture requirements.
• Prepare governance documentation and recommendations for review.

Skills

Data governance

Data quality

Metadata management

Stakeholder collaboration

Education

Bachelor's degree or military equivalent

Tools

Data catalogs

Data quality platforms

Reporting tools

SQL basics

Job description

Dow seeks a Data Governance Analyst to support enterprise governance practices. Located in Midland, MI or Houston, TX, the role focuses on governance artifacts, metadata, data quality, and stewardship across business domains.

The successful candidate partners with business and technical teams to improve usability of enterprise data assets, ensuring governance standards are consistently applied across domains.