Data Quality Engineer
Data Quality Engineers ensure data is accurate, complete, and consistent. They design quality frameworks and monitor data health.
Median Salary
$145,000
Job Growth
High — data quality is fundamental to good decisions
Experience Level
Entry to Leadership
Salary Progression
| Experience Level | Annual Salary |
|---|---|
| Entry Level | $100,000 |
| Mid-Level (5-8 years) | $145,000 |
| Senior (8-12 years) | $175,000 |
| Leadership / Principal | $205,000+ |
What Does a Data Quality Engineer Do?
Data Quality Engineers establish frameworks ensuring data reliability. They define quality metrics and thresholds for each dataset. They implement automated tests catching quality issues. They monitor data continuously for anomalies. They investigate quality failures and work with upstream teams on fixes. They educate organization about data quality importance. They balance between preventing bad data and enabling data flow.
A Typical Day
Metric definition: Define quality metrics for customer database. What does good data look like?
Test implementation: Write automated tests for common quality issues.
Monitoring: Set up monitoring dashboard tracking quality metrics.
Investigation: Investigate quality issues. Find root causes.
Collaboration: Work with upstream teams to fix issues at source.
Documentation: Document data quality standards and expectations.
Training: Educate organization about data quality.
Key Skills
Career Progression
Data quality engineers often specialize in specific domains. Senior engineers establish quality culture across organizations.
How to Get Started
SQL & Python: Strong data querying and scripting skills.
Statistics: Statistical thinking. Understand distributions and anomalies.
Quality tools: Great Expectations, Databand, or similar tools.
Domain expertise: Understand the data and business domain you're working in.
Problem solving: Good debugging and investigation skills.
Communication: Explain quality issues to non-technical stakeholders.
Real data: Work with real datasets. Understand actual quality challenges.
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What does data quality mean?▼
Data is accurate (correct), complete (no missing), consistent (no contradictions), and timely (current). Quality is multidimensional.
Why does data quality matter?▼
Bad data leads to wrong decisions. Models trained on bad data underperform. Bad data in production breaks features. Quality is foundational.
What are common data quality issues?▼
Missing values, outliers, duplicates, schema violations, stale data, inconsistencies across systems, unrealistic values.
How do you measure data quality?▼
Metrics: completeness (% missing), accuracy (% correct), consistency (cross-system agreement), timeliness (how fresh).
Is data quality enforcement automated or manual?▼
Mix of both. Automated tests catch many issues. Human expertise identifies edge cases and evaluates domain-specific quality.
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Last updated: 2026-03-07