Data Quality Manager

Career Guide

A Data Quality Manager ensures that an organization’s data is accurate, consistent, complete, and trusted for reporting, analytics, operations, and customer experiences. The role combines governance, process design, monitoring, and cross team coordination to prevent data issues and resolve them quickly when they occur.

Key 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

Audit one critical data set and publish a simple quality scorecard

Define a data issue workflow with clear ownership and response times

Implement automated validation checks for key tables and fields

Create a basic data dictionary for the most used metrics and fields

Partner with data engineering to add monitoring and alerting for failures

Run a quarterly root cause review and track defect reduction over time

Build a short training session on common data entry and data usage errors

Document data owners for priority domains and align on quality targets