emorain3/rebate-intelligence-pipeline-casestudy

# Rebate Intelligence Pipeline A data engineering case study built in five days as part of a technical skills assessment. The problem: a regional network of independent collision repair shops was processing rebate data manually — a slow, error-prone process that made it nearly impossible to catch missed payments or flag suspicious activity. This project is my proposed solution. ## What's in here: - **Power BI dashboard** — three pages surfacing silent shops, anomaly flags, and an executive summary - **Case study site** — a walkthrough of the business problem, engineering decisions, and what I learned ## The case study 👉 [https://emorain3.github.io/rebate-intelligence-pipeline-casestudy/](https://emorain3.github.io/rebate-intelligence-pipeline-casestudy/) ## The Python Pipeline can be found here: - [https://github.com/emorain3/rebate-intelligence-pipeline](https://github.com/emorain3/rebate-intelligence-pipeline) ## Stack Python · Power BI · Microsoft Fabric (architecture) · Medallion / Bronze-Silver-Gold ## Important Note: The dataset used in this project was provided as part of a skills assessment. The organization name and select variable names have been anonymized to protect proprietary operations. The pipeline logic, architecture decisions, and dashboard design are entirely my own work. ## About me I'm a Cloud Data Engineer based in Metro Atlanta. I work across Azure and GCP with a focus on building data systems that are reliable, auditable, and actually useful to the people depending on them. [LinkedIn](https://linkedin.com/in/ecclesiamorain)

标签：后端开发