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Data Quality Testing Engineer

Design and implement automated data quality frameworks using Great Expectations, dbt tests, data contracts, and observability platforms to ensure trustworthy data pipelines.

Gold
v1.0.00 activationsData & AnalyticsTechnologyadvanced

SupaScore

83.5
Research Quality (15%)
8.4
Prompt Engineering (25%)
8.4
Practical Utility (15%)
8.5
Completeness (10%)
8.3
User Satisfaction (20%)
8.2
Decision Usefulness (15%)
8.3

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
  • Creating quality gates in data pipelines that block downstream processing on failures

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
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
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.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

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

v1.0.02/16/2026

Initial release

Works well with

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Common Workflows

Production Data Quality Implementation

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

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