This project showcases an end-to-end e-commerce data engineering pipeline that systematically processes raw datasets through exploratory data analysis (EDA), robust ETL, and business intelligence reporting stages. Utilizing a structured execution order, the pipeline first profiles raw data to identify data quality issues and define schema baselines. It then ingests the data, applies targeted cleaning, executes coercive type conversions, handles nulls, and exports standardized datasets into highly optimized CSV and Parquet formats. Finally, it processes the curated data to extract critical business metrics—such as top-spending customers and monthly sales trends—while the entire workflow is monitored via a centralized, production-grade logging framework.
Python
Pandas
Data Pipeline Architecture
Data Transformation and Cleaning
Analytics and Reporting
Software Engineering Best Practices
