A data engineer built an AWS-native pipeline to solve a common problem: moving raw Google Analytics 4 data into a usable format. The architecture uses Python scripts to extract specific KPIs via the GA4 API, landing the raw JSON/CSV files in Amazon S3 as a durable data lake before ingestion.

From S3, data is loaded into Amazon Redshift using the `COPY` command. The engineer optimized ETL processes to achieve 98% data accuracy while reducing errors by 35%. This creates a centralized 'Single Source of Truth' for business strategy, moving away from fragmented, manual data handling.

The business impact was direct: manual reporting time was cut by 50%, and data availability increased by 40%. Executives now access real-time insights through Apache Superset dashboards. The project underscores that automation is key to scalability in cloud data engineering, especially when mastering foundational AWS services like S3 and Redshift.