The Problem
Manual data ingestion from 15+ sources was consuming 40 hours per week. Data quality issues were slipping through, impacting downstream analytics and BI dashboards. The team needed a reliable, automated solution that could scale.
My Approach
I architected an end-to-end ELT pipeline using Apache Airflow with Python transformations. The design prioritized reliability, monitoring, and self-healing capabilities. I implemented incremental loading patterns, idempotent operations, and comprehensive error handling.
What I Built
- Apache Airflow DAGs orchestrating 15+ data connectors
- Python transformation layer handling CRM, ad platform, and web analytics data
- Automated retry logic and failure notifications
- Data quality checks and anomaly detection
- Comprehensive documentation and runbooks for ops team
Impact & Results
75%
Time savings (40h → 10h/week)
2TB+
Daily data volume handled
99.5%
Pipeline uptime
15+
Data sources integrated
Tech Stack
Apache AirflowPythonRedshiftAWSCI/CDDocker
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