About the role
A Data Quality Manager job description covers leading a data management team and setting standards that keep company data accurate, complete, and consistent. The role protects sound business decisions and compliance with data protection requirements. Hiring managers should assess data analysis, troubleshooting, SQL, and AI fluency.
The Data Quality Manager sets the standards and controls that keep data reliable and works with IT and data teams to improve it. Employers may also post this role as Data Governance Manager or Data Quality Lead. Core duties include:
Standards. Develop and implement data quality standards and data protection measures.
Monitoring. Monitor and analyze data quality, and bring attention to areas of improvement.
Corrective action. Troubleshoot data-related issues and establish corrective actions.
What are the essential Data Quality Manager skills?
The essential skills are data analysis and quality monitoring, troubleshooting data issues, project management, querying data with SQL, and AI fluency.
Skill | Why it matters | Bryq assessment and level |
|---|---|---|
Data analysis and quality monitoring | Reveals where data quality needs to improve. | Data Analysis (advanced) |
Troubleshooting data issues and corrective actions | Finds the cause of data problems and fixes them. | Troubleshooting (advanced) |
Project management | Keeps data control initiatives on track across teams. | Project Management (intermediate) |
Querying and validating data in databases | Confirms data is accurate and complete at the source. | SQL (advanced) |
AI Fluency | Supports profiling, anomaly detection, and documentation. | AI Fluency (functional) |
The role’s core work is monitoring and analyzing data quality, troubleshooting problems, and setting corrective actions. Project management helps the manager lead data control initiatives with IT and data teams.
Levels are a starting point. Bryq lets you set the level each role needs.
Which nice-to-have skills help a Data Quality Manager stand out?
Risk management and data protection, data visualization and reporting, scripting, and translating data concepts for business stakeholders add value but are not needed on day one.
Risk management and data protection. Strengthens data controls and compliance.
Data visualization and reporting. Makes quality findings easy to share.
Scripting for data validation and automation. Speeds up repeated quality checks.
Translating data concepts for business stakeholders. Helps stakeholders act on quality findings.
Use these skills as tie-breakers between candidates who are equally strong on the essentials. Never screen out candidates who can learn them on the job.
Which competencies predict success as a Data Quality Manager?
The essential competencies are Leadership Potential and Communication.
Leadership Potential. Trains the team, sets standards, and guides policy adoption.
Communication. Translates data concepts and documents findings clearly.
Empathy adds understanding of stakeholder needs, and Learning Agility helps the manager keep pace with data tools.
What AI skills does a Data Quality Manager need?
A Data Quality Manager should use AI to profile data, draft documentation, and diagnose recurring errors, because these tools increasingly support daily data quality work. Strong AI fluency lets the manager apply them without building them.
AI helps with data profiling, anomaly detection, and documentation, so the manager uses it daily. AI output can be wrong, so the manager verifies results against source systems before acting.
Profiling. Use AI-assisted profiling tools to detect duplicates, missing values, and anomalies across large datasets.
Drafting. Use generative AI to draft data quality standards, training materials, and plain-language summaries of quality reports for stakeholders.
Root causes. Use AI to suggest root causes and corrective actions for recurring data errors, then validate them against source systems.
How do you assess Data Quality Manager candidates with Bryq?
Bryq measures all the essential skills and traits for a Data Quality Manager in one integrated candidate profile: cognitive ability, personality traits, soft and hard skills, including AI fluency. Bryq builds an ideal profile for the role and matches candidates against it.
Profile Fit. Profile Fit measures cognitive ability and personality, matched against the ideal profile for the role.
Data Analysis. Data Analysis measures data analysis and quality monitoring skills.
Troubleshooting. Troubleshooting measures how candidates diagnose data issues and choose corrective actions.
Project Management. Project Management measures planning and delivery of data control initiatives.
SQL. SQL measures querying and validating data in databases.
AI Fluency Assessment. The AI Fluency Assessment measures how well candidates apply AI tools and verify their output.
Optional add-ons. Use interview guides for structured follow-up conversations.
Competency profile. Leadership Potential and Communication are essential, with Empathy and Learning Agility as optional additions, through competency mapping.
All Bryq assessments are validated by I/O psychologists. Profile Fit is independently bias-audited under New York City Local Law 144 (code4thought, April 2026). The AI Fluency Assessment is built on peer-reviewed and subject-matter expert research. A person makes every hiring decision.
Data Quality Manager (Data Governance Manager) job description template
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Data Quality Manager |
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About [Company name][Add a short description of your company.] About the roleAs our Data Quality Manager, you will lead the data management team and make sure our company data is accurate, complete, and consistent. You will set the standards and controls that support sound business decisions and data protection compliance. You will work closely with IT, data teams, and business stakeholders. Responsibilities
Qualifications
Skills and competencies we look for
Working with AI
What we offer
Equal opportunity[Add your equal opportunity statement.] |
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Author
The Bryq I/O Team
Bryq’s I/O Psychology Team is a group of industrial-organizational psychologists who design and validate the science behind our assessments. They write about what research says on hiring, skills, personality, and performance, and how HR leaders can apply it to make fairer, evidence-based decisions.
