You've implemented several end‑to‑end performance optimizations. Can you describe your experience and the services you used?

I have led end‑to‑end performance optimization projects across multiple stacks. For example, I optimized a data pipeline by introducing Amazon Kinesis Data Streams for real‑time ingestion, replacing batch ETL jobs with Lambda functions, and using Amazon DynamoDB with provisioned throughput and on‑demand scaling. I also implemented caching with Amazon ElastiCache (Redis) to reduce database load, and used CloudFront to cache static assets globally. On the application side, I applied query optimization techniques, added indexes, and refactored code to reduce latency. I monitored performance with CloudWatch and used AWS X‑Ray to trace slow requests. The result was a 40% reduction in end‑to‑end latency and a 30% cost savings on compute resources. In another project, I introduced a CDN and edge computing with Lambda@Edge to offload processing, which cut response times by 25%. I also performed load testing with Artillery and used Auto Scaling policies to handle traffic spikes. These optimizations not only improved user experience but also increased system reliability and reduced operational costs.