Data Quality Manager
Career GuideKey Responsibilities
- Define data quality standards and rules for critical data sets
- Create and maintain data quality scorecards and key metrics
- Monitor data pipelines and dashboards for quality issues
- Lead root cause analysis for recurring data defects
- Coordinate fixes with data engineering, analytics, and business teams
- Set up data validation checks and exception handling workflows
- Manage a data issue intake process and prioritization queue
- Build and maintain a data quality roadmap tied to business outcomes
- Establish data ownership and accountability for key data domains
- Support audits, compliance requests, and data risk reviews
- Train teams on data quality practices and documentation
- Report data quality status to leadership with clear, actionable updates
Top Skills for Success
Stakeholder Management
Clear Communication
Process Improvement
Problem Solving
Data Governance
Data Quality Measurement
Data Profiling
Data Lineage Documentation
Metadata Management
SQL
Data Validation Design
Root Cause Analysis
Risk Management
Data Modeling Fundamentals
ETL Monitoring
Career Progression
Can Lead To
Senior Data Quality Manager
Data Governance Manager
Data Platform Manager
Analytics Engineering Manager
Data Operations Manager
Transition Opportunities
Head of Data Governance
Director of Data Management
Director of Analytics
Chief Data Officer
Data Product Manager
Common Skill Gaps
Often Missing Skills
Data Quality FrameworksData Observability ToolsData Catalog ToolsMaster Data ManagementData Incident ManagementMetric DefinitionChange ManagementAutomated Testing for Data Pipelines
Development SuggestionsFocus on building repeatable quality rules for high value data sets, learn how to set up automated checks and alerts, and practice translating technical findings into business impact. Pair this with hands on SQL and experience with a data catalog to strengthen ownership, lineage, and accountability.
Salary & Demand
Median Salary Range
Entry LevelUSD 85,000 to 110,000
Mid LevelUSD 110,000 to 145,000
Senior LevelUSD 145,000 to 190,000
Growth Trend
Steady to strong growth. Hiring is driven by increased reliance on analytics, AI initiatives, regulatory expectations, and the need to reduce operational errors from poor data.Companies Hiring
Major Employers
AmazonMicrosoftGoogleWalmartJPMorgan ChaseBank of AmericaUnitedHealth GroupCVS HealthPfizerComcastDeloitteAccenture
Industry Sectors
Financial ServicesHealthcareInsuranceRetail and EcommerceTechnologyTelecommunicationsManufacturingLogisticsEnergyPublic SectorConsulting Services
Recommended Next Steps
1
Audit one critical data set and publish a simple quality scorecard2
Define a data issue workflow with clear ownership and response times3
Implement automated validation checks for key tables and fields4
Create a basic data dictionary for the most used metrics and fields5
Partner with data engineering to add monitoring and alerting for failures6
Run a quarterly root cause review and track defect reduction over time7
Build a short training session on common data entry and data usage errors8
Document data owners for priority domains and align on quality targets