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Data & AnalyticsTechnologyPlatinum

Automate data quality checks in data pipelines.

Data Quality Testing Engineer

Great Expectations, dbt, data contracts

advancedv5.0

Best for

  • Setting up automated data quality checks using Great Expectations on critical production datasets
  • Building dbt test suites with custom business logic validations for data transformations
  • Implementing data contracts between teams with SLA monitoring and alerting
  • Designing data observability dashboards to catch schema drift and volume anomalies

What you'll get

  • Great Expectations suite configurations with specific expectation definitions for business rules and statistical thresholds
  • dbt test implementations with custom macros for complex cross-table validations and data lineage checks
  • Data contract specifications with schema definitions, SLA requirements, and automated monitoring setup
Expects

Details about your data stack, critical data assets, current quality issues, and business impact of data failures.

Returns

Technical implementation plans for automated quality frameworks, test specifications, monitoring configurations, and governance processes.

What's inside

You are a Data Quality Engineer. You design and implement automated quality frameworks that reduce data incidents by 80%+, establish data contracts across teams, and monitor petabytes of data. - Classify data assets by business criticality (Tier 1/2/3) and invest quality effort where it matters most...

Covers

What You Do DifferentlyMethodologyWatch For
Not designed for ↓
  • ×Manual data cleaning or one-off data fixes
  • ×Statistical modeling or machine learning algorithm development
  • ×Database administration or performance tuning
  • ×Business intelligence dashboard creation

SupaScore

87.93
Research Quality (15%)
8.85
Prompt Engineering (25%)
8.9
Practical Utility (15%)
8.55
Completeness (10%)
9.2
User Satisfaction (20%)
8.7
Decision Usefulness (15%)
8.65

Evidence Policy

Standard: no explicit evidence policy.

data-qualitygreat-expectationsdbt-testsdata-contractsdata-observabilitydata-testingpipeline-qualityschema-validationanomaly-detectiondata-governancesodadata-reliability

Research Foundation: 7 sources (4 official docs, 2 books, 1 industry frameworks)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v5.03/25/2026

v5.5 final distill

v2.02/21/2026

Pipeline v4: rebuilt with 3 helper skills

v1.0.02/16/2026

Initial release

Works well with

Need more depth?

Specialist skills that go deeper in areas this skill touches.

Common Workflows

Production Data Quality Implementation

Design data architecture, implement quality testing framework, then set up comprehensive monitoring and alerting

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