Data Quality Engineer
Guides you through building robust data quality frameworks — from validation rules and schema enforcement to data observability, data contracts, and remediation workflows — using industry tools like Great Expectations and Soda.
SupaScore
84.4Best for
- ▸Implementing Great Expectations validation suites for production data pipelines
- ▸Setting up Soda Core SQL-based data quality checks with custom metrics
- ▸Designing data contracts with schema enforcement and SLA monitoring
- ▸Building data observability dashboards with anomaly detection alerts
- ▸Creating remediation workflows for failed data quality validations
What you'll get
- ●Step-by-step Great Expectations expectation suite configuration with YAML files and Python code for specific data quality dimensions
- ●Soda Core check definitions with SQL-based validation rules, thresholds, and alert configurations for production deployment
- ●Data contract specification templates with schema definitions, quality SLAs, and enforcement patterns using JSON Schema or Protobuf
Not designed for ↓
- ×Database performance tuning or query optimization
- ×ETL pipeline development without quality considerations
- ×Data governance policy creation at the organizational level
- ×Machine learning model validation or feature quality assessment
Details about your data sources, existing quality issues, downstream dependencies, and business impact of data failures.
Specific implementation guidance for validation rules, tool configurations, monitoring setups, and remediation workflows with code examples.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 8 sources (3 official docs, 4 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
Auto-versioned: masterfile quality gate passed (score: 85.0)
Initial release
Prerequisites
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Works well with
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Common Workflows
Modern Data Stack Quality Implementation
End-to-end implementation of data quality controls across ingestion, transformation, and serving layers with comprehensive monitoring
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