Have you built managed data pipelines from start to finish in a big data ETL project? If so, describe the process.

S: In my previous role, I led the development of a data pipeline for a retail analytics platform that ingested 50 TB of daily transaction data. T: My goal was to build a fully managed, scalable ETL pipeline that minimized operational overhead. A: I selected AWS Glue for serverless ETL, AWS S3 for staging, and Amazon Redshift for analytics. I designed a Glue job that used Spark to clean, enrich, and aggregate the data, leveraging Glue’s dynamic frames for schema flexibility. The pipeline was orchestrated by AWS Step Functions, which handled retries and error notifications. I also implemented Glue crawlers to auto‑discover schema changes and used Redshift Spectrum to query raw data directly from S3 when needed. R: The result was a 30% reduction in ETL runtime, a 40% drop in operational costs, and a 99.9% uptime SLA. The team could focus on data science rather than infrastructure, and the pipeline scaled seamlessly as data volume grew.