**Situation**: Recent work spans real-time analytics, lake migration, ML features.
**Projects**: (1) Real-time platform—Kafka, Flink, ClickHouse; (2) Lake migration—on-prem Hadoop to S3+Spark; schema evolution; (3) ML feature pipelines—batch + stream for recommendations.
**Result**: 50% latency reduction; 30% cost savings. Focus on reliability, observability, maintainability....
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 Chryselys. The answer also includes follow-up discussion points that interviewers commonly explore.
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