Great Expectations Data Quality Engineer
Design and implement automated data quality validation pipelines using Great Expectations, with expectation suite design, threshold calibration, and integration into dbt, Airflow, and Spark workflows.
SupaScore
83.9Best for
- ▸Building automated data validation pipelines with Great Expectations for Snowflake, BigQuery, or Spark data warehouses
- ▸Designing expectation suites that catch data quality issues without generating false positive alerts
- ▸Integrating GX checkpoints into dbt model tests and Airflow DAG orchestration workflows
- ▸Setting up data quality monitoring dashboards with threshold calibration based on historical data patterns
- ▸Implementing data contracts with automated validation for upstream data sources and downstream consumers
What you'll get
- ●Complete expectation suite Python code with column-level and table-level validations, including custom expectations for business rules
- ●Checkpoint configuration files with action lists for Slack notifications, data docs updates, and pipeline failure handling
- ●Integration code showing how to embed GX validations into dbt tests, Airflow operators, and Spark job workflows
Not designed for ↓
- ×Generic data profiling without automated validation pipeline implementation
- ×Statistical analysis or machine learning model validation beyond data quality checks
- ×Data governance policy creation without technical implementation components
- ×ETL pipeline development that doesn't involve data quality validation layers
Data pipeline architecture details, sample datasets, existing data quality pain points, and integration requirements with tools like dbt, Airflow, or Spark.
Complete Great Expectations implementation plans including expectation suite code, checkpoint configurations, integration patterns, and threshold calibration strategies.
Evidence Policy
Enabled: this skill cites sources and distinguishes evidence from opinion.
Research Foundation: 8 sources (3 official docs, 2 books, 2 industry frameworks, 1 academic)
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
Prerequisites
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Works well with
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Common Workflows
Data Quality Pipeline Implementation
Complete data pipeline with embedded quality validation from ingestion through transformation to serving
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