Automate data quality checks in data pipelines.
Data Quality Testing Engineer
Great Expectations, dbt, data contracts
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
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.
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
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▼
Evidence Policy
Standard: no explicit evidence policy.
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
v5.5 final distill
Pipeline v4: rebuilt with 3 helper skills
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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