All work
Data Engineering
Enterprise Analytics Pipeline
Built an automated data engineering workflow for analytics, using Python and Apache Airflow for orchestration, Docker for containerization, AWS S3 and Redshift for storage and analytics, and Power BI for downstream reporting.
Business problem
Operational data lived in spreadsheets and needed a reliable, automated path into analytics and reporting.
Solution
- Built an automated pipeline from Google Sheets through Python and Airflow into AWS S3 and Amazon Redshift, feeding Power BI.
Measurable impact
Automated
Source-to-dashboard data flow
Technical implementation
- Python and Apache Airflow for orchestration.
- Docker for containerization.
- AWS S3 and Amazon Redshift for storage and analytics.
- Power BI for downstream reporting.
- Python
- Apache Airflow
- Docker
- AWS
- Redshift
- Power BI
Have a data problem that's taking too much time?
Whether it's a manual reporting process, disconnected data or an analytics bottleneck, I build systems that turn that complexity into something useful.