How does your data pipeline handle late‑arriving data and corrupted aggregations, and can you describe a specific instance where you mentored a junior engineer through a bottleneck in your implementation?

To handle late‑arriving data we use event time windows with watermarks and allow a small lateness period. Corrupted records are filtered or sent to a dead‑letter queue so they don’t corrupt aggregates. In a recent project, a junior engineer struggled with a slow aggregation step caused by a large late‑arrival burst. I guided him to add a watermark, reduce the window size, and partition the stream by key, which cut the aggregation time from 12 minutes to 2 minutes. I also introduced a monitoring dashboard that alerts on lag, so the bottleneck was identified early.