Context
A retail client needed to consolidate transaction data from multiple point-of-sale systems into a unified warehouse. Data sources were inconsistent, and manual reports caused delays and errors across store locations.
My Role
Data engineer — designed the ETL pipeline architecture, implemented data ingestion scripts, and built the reporting dashboards that store managers rely on daily.
Approach
I modelled the relational schema to accommodate varied source formats, then used Python and SQL to extract and transform the data before loading it into a MariaDB warehouse. I built Tableau dashboards to surface sales trends and anomalies at a glance.
Technical Stack
- Languages: Python, SQL
- Database: MariaDB (warehouse)
- Visualization: Tableau
- Orchestration: Cron-based scheduling with email alerting on failures
Impact
The automated pipeline reduced reporting time by 75% and improved data accuracy by 25%. Store managers gained access to up-to-date metrics, enabling faster, more confident decisions across locations.
What's Next
- Implement real-time ingestion using message queues for near-instant visibility.
- Add anomaly detection to flag unusual transaction patterns automatically.
- Enable self-service analytics so non-technical stakeholders can build their own views.