Data engineering capstone · 2026
SupplyChain360 Unified Data Platform
An automated ELT platform pulling products, inventory, shipments and sales from S3, Google Sheets and Postgres into Redshift, ready for Power BI every morning.
PythonApache AirflowAWS S3RedshiftdbtDockerPower BI
Context
Supply chain operations were generating fragmented data across different legacy systems, causing delays in reporting and creating operational bottlenecks.
The question
How can we centralise and automate the data flow so stakeholders have a single, reliable source of truth for inventory and logistics without relying on slow, manual data extracts?
Approach
- 1Designed and implemented a scalable ELT pipeline to extract raw operational data from multiple sources: S3 buckets, Google Sheets and a Postgres database.
- 2Orchestrated ingestion with Apache Airflow in Docker, landing data in an S3 raw layer before loading it into Amazon Redshift.
- 3Built dbt transformation models to clean, standardise and model the data for business intelligence.
Outcome
- Fresh, reliable data delivered to executive dashboards automatically every morning.
- Eliminated manual reporting errors and engineering bottlenecks, so leadership can make faster, more confident logistics decisions.
