Describe the key functionalities of your data pipeline: the source system, the ingestion mechanism you use to bring data into cloud storage, the transformations and cleansing performed in the silver layer, the aggregation performed in the gold layer, and the consumers or users that consume the data. What is the business use case?

In my last role I built a lakehouse on AWS. The source was a legacy relational database and a set of REST APIs. I used AWS DMS and Kafka Connect to ingest change data capture (CDC) into an S3 bucket, which served as the bronze layer. In the silver layer I ran AWS Glue jobs that performed schema evolution, deduplication, and field‑level cleansing (e.g., normalizing phone numbers). The gold layer was populated by Spark Structured Streaming jobs that aggregated daily sales per region and enriched the data with external weather feeds. The final datasets were exposed via Amazon Athena and a Snowflake data warehouse. Business users—product managers and analysts—queried the gold tables to drive pricing decisions and inventory forecasting. The pipeline was monitored with CloudWatch and automated alerts for data quality violations.