**Experience**: Production data engineering on AWS (S3, Glue, Lambda, EMR, Athena, Kinesis), GCP (BigQuery, Dataflow, Composer), Azure (ADF, Synapse). Built data lakes (medallion), real-time pipelines (Kinesis→Lambda→S3), batch ETL (Glue, Dataflow). IaC with Terraform; CI/CD for pipeline deployment. **Focus**: Cost optimization (Spot, compression, right-sizing), security (encryption, IAM, audit), reliability (idempotency, monitoring)....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Thoughtworks. The answer also includes follow-up discussion points that interviewers commonly explore.
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