Data Processing & ETL Framework
A Python-based ETL framework for processing large-scale data from multiple sources (APIs, databases, files), applying transformations, running quality checks, and loading into a data warehouse.
🎯Problem
Multiple legacy data pipelines were failing silently, delivering stale and incorrect data to business dashboards.
💡Solution
Rewrote all pipelines using a unified ETL framework with comprehensive logging, data quality assertions, and automated alerts.
🏗️Architecture
Airflow orchestrates daily ETL runs. Python extraction modules pull data from sources, pandas transforms and validates records, SQLAlchemy loads to PostgreSQL. Data quality checks run as separate tasks with anomaly detection.
⚠️Challenges
Handling schema evolution in source systems without breaking downstream pipelines required a flexible schema registry approach.
📚Lessons Learned
Data contracts between teams prevent most pipeline failures. Monitoring data freshness is as important as monitoring errors.