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Data Governance Jobs in Seattle, WA (NOW HIRING)

Job Summary We are seeking a Data Governance Analyst to help drive enterprise data governance initiatives across the organization. This role will partner with data owners, data stewards, and ...

Information Governance Team's Mission Weyerhaeuser recognizes the critical role of data, analytics, and reporting in driving business success and gaining a competitive advantage. Our mission is to ...

Data Governance- Manager

Seattle, WA · On-site

$99K - $232K/yr

The Opportunity As part of the Data Governance team, you will lead the development and implementation of data-driven strategies that drive business growth and enhance decision-making. As a Manager ...

Sr. Product Manager, Data Governance

Seattle, WA · On-site

$144K - $190K/yr

Responsibilities : • Design and launch new governance and security capabilities as part of Unity Catalog. • Define and build new platform services that enable Databricks' data & AI product teams ...

Data Governance * Data Modernization * Advanced Analytics * Data Visualization The ideal candidate's experience may include but is not limited to the following: * Have experience understanding and ...

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Data Governance information

See Seattle, WA salary details

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How much do data governance jobs pay per hour?

As of Aug 17, 2026, the average hourly pay for data governance in Seattle, WA is $62.34, according to ZipRecruiter salary data. Most workers in this role earn between $46.25 and $76.35 per hour, depending on experience, location, and employer.

What is a data governance?

A Data Governance job focuses on managing the availability, integrity, security, and usability of an organization's data. Professionals in this role establish policies, processes, and standards to ensure data quality and compliance with regulations. They collaborate with various teams to define data ownership, improve data practices, and mitigate risks. The goal is to maximize the value of data while ensuring it is secure and reliable for decision-making.

Is data governance a good career path?

Data governance is a viable career path involving managing data quality, security, and compliance within organizations. It requires skills in data management, understanding regulations, and often familiarity with tools like data catalogs and metadata management systems. The field offers opportunities for advancement and specialization in industries that prioritize data integrity and privacy.

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

To thrive in Data Governance, you need a strong understanding of data management principles, data quality frameworks, and relevant regulatory requirements, often supported by a degree in information systems or a related field. Familiarity with data governance platforms (such as Collibra or Informatica), data mapping tools, and relevant certifications (like CDMP or DGSP) is highly beneficial. Attention to detail, stakeholder management, and effective communication are standout soft skills for this role. These capabilities ensure organizations can maintain high data quality, comply with regulations, and support informed business decision-making.

What are the typical responsibilities of someone working in data governance?

Professionals in Data Governance are responsible for establishing and enforcing data policies, standards, and procedures to ensure data accuracy, consistency, and security across the organization. They work closely with data owners, stewards, and IT teams to define data definitions, resolve data quality issues, and manage data lifecycle processes. Daily tasks often include conducting audits, facilitating data governance meetings, and providing guidance on regulatory compliance. This role is highly collaborative and often serves as the bridge between business and technical stakeholders, ensuring data assets are used responsibly and effectively.

What do data governance jobs do?

Data governance jobs involve establishing and maintaining policies, standards, and procedures to ensure data quality, security, and compliance within an organization. Professionals in this field often work with data management tools, collaborate with IT and business teams, and may hold certifications like CDMP or DAMA to support effective data stewardship and regulatory adherence.

What are the most commonly searched types of Data Governance jobs in Seattle, WA?

The most popular types of Data Governance jobs in Seattle, WA are:

What job categories do people searching Data Governance jobs in Seattle, WA look for?

The top searched job categories for Data Governance jobs in Seattle, WA are:

What cities near Seattle, WA are hiring for Data Governance jobs?

Cities near Seattle, WA with the most Data Governance job openings:

Infographic showing various Data Governance job openings in Seattle, WA as of August 2026, with employment types broken down into 74% Full Time, 5% Part Time, and 21% Contract. Highlights an 95% In-person, and 5% Remote job distribution, with an average salary of $129,666 per year, or $62.3 per hour.

Data Governance Analyst

Wimmer Solutions

Renton, WA • On-site

Other

Posted 4 days ago


Job description

Job Summary

We are seeking a Data Governance Analyst to help drive enterprise data governance initiatives across the organization. This role will partner with data owners, data stewards, and technical teams to improve data visibility, quality, lineage, and management within our data catalog and data platform ecosystem. The analyst will support metadata management, source system rationalization, data quality implementation, and lifecycle management activities to ensure trusted, well-governed enterprise data assets.

Key Responsibilities

·        Metadata & Data Catalog Management

·        Research and analyze enterprise data sources to identify common systems of record and establish relationships between related assets within the data governance platform

·        Maintain and enhance linked-record relationships within the data governance team’s workflow tool to improve data discovery, lineage, and understanding across the organization.

·        Partner with data owners and business stakeholders to document and maintain metadata for enterprise data assets.

·        Facilitate the assignment of data domains, ownership, stewardship, and security classifications within the data catalog.

Data Lifecycle & Source Rationalization

·        Support end-of-life (EOL) activities for source systems and databases being retired or replaced.

·        Analyze downstream dependencies and identify users, reports, applications, and data products consuming retired data assets.

·        Coordinate migration activities from legacy data sources to replacement platforms and replicated datasets.

·        Work with technical teams to decommission obsolete Snowflake schemas, replication processes, and associated metadata.

Data Catalog Adoption & Enablement

·        Develop deep expertise in the Acryl Data platform and serve as a subject matter expert for business users, data owners, and data stewards.

·        Provide training, guidance, and day-to-day support for users of the enterprise data catalog.

·        Create job aids, documentation, and best practices to improve adoption and effective use of governance tools.

·        Partner with stakeholders to onboard new domains, teams, and data assets into the catalog.

·        Promote governance processes and encourage consistent use of metadata, lineage, ownership, classification, and data quality capabilities.

·        Act as the primary point of contact for catalog-related questions, troubleshooting, and user support.

Data Quality Program Support

·        Collaborate with data owners and stewards to define data quality requirements and key business rules.

·        Configure, implement, and monitor data quality checks within the enterprise data catalog and governance platform.

·        Track data quality issues and partner with business and technical teams to drive remediation efforts.

·        Develop metrics and reporting to measure adoption and effectiveness of data quality controls.

Stakeholder Engagement

·        Build relationships with business and technical stakeholders across multiple domains.

·        Educate data owners and stewards on governance processes, metadata standards, and data quality best practices.

·        Facilitate meetings and workshops to gather requirements, review governance deliverables, and support adoption initiatives.

·        Assist in developing governance documentation, standards, and procedures.

Required Qualifications

·        Bachelor’s degree in information systems, Data Management, Computer Science, Business, or related field, or equivalent experience.

·        3+ years of experience in Data Governance, Data Management, Business Intelligence, Data Analysis, or related disciplines.

·        Experience working with enterprise data catalogs and metadata management tools.

·        Understanding of data lineage, data ownership, data stewardship, and data governance frameworks.

·        Experience working with data warehouses and cloud platforms such as Snowflake.

·        Strong analytical and problem-solving skills.

·        Ability to communicate effectively with both technical and business stakeholders.

·        Experience coordinating cross-functional initiatives and driving stakeholder engagement.

Preferred Qualifications

·        Experience with data governance platforms such as Acryl Data, Microsoft Purview, Collibra, Alation, Informatica, or similar tools.

·        Familiarity with data classification, privacy, and information security concepts.

·        Experience implementing or monitoring data quality rules and controls.

·        Understanding of ETL/ELT processes, data replication, and system integrations.

·        Knowledge of SQL and data analysis techniques.

Success Measures

·        Increased percentage of cataloged assets with assigned ownership, domain, and security classifications.

·        Improved linkage and lineage accuracy across enterprise data assets.

·        Successful retirement of legacy data sources with minimal business disruption.

·        Increased adoption of data quality monitoring and stewardship practices.

·        Reduction in orphaned, duplicate, or unmanaged data assets.