Tell me about a time when you had to troubleshoot a complex issue in a production data pipeline. What tools and techniques did you use to resolve the issue?

S: In my role as a Data Engineer at an e‑commerce company, our nightly ingestion pipeline from Kafka to Snowflake started showing a 5‑minute latency spike and intermittent missing records after a schema change. T: My task was to identify the root cause and restore the pipeline within the 30‑minute SLA. A: I first used Datadog to monitor pipeline metrics and trace logs across all stages. I checked Kafka consumer lag with Kafka Manager and verified offsets. I then reviewed the Confluent Schema Registry and discovered that the producer had updated the schema to a new version that the consumer had not yet migrated to, causing deserialization failures. I rolled back the schema change in the registry, updated the consumer code to handle the new version, and added schema validation checks. I also implemented an automated retry mechanism and enhanced alerting for future schema changes. R: The latency dropped to under 200 ms, data integrity was fully restored, and we met the SLA. I learned the importance of versioned schemas, automated tests, and real‑time monitoring for production pipelines.