Project

Data Quality Validation for ETL Pipelines

Implemented validation checks and standardized transformations to reduce reporting errors.

Data QualityETLValidation
PythonPySparkSQL

Highlights

  • Added checks for nulls, duplicates, and schema drift.
  • Reduced manual clean-up for reporting teams.
  • Documented validation logic for audit-ready reporting.

Outcome

Reporting teams spend less time troubleshooting data issues thanks to consistent validation steps.

What I did

  • Embedded data quality checks in ETL workflows.
  • Created reusable validation utilities for multiple pipelines.
  • Shared validation results with stakeholders for transparency.

Next steps

Automate daily data quality summaries for KPI owners.