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.
SupaScore
83.5Best 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
Details about your data stack, critical data assets, current quality issues, and business impact of data failures.
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.
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
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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