What day‑to‑day challenges have you faced?

S: While working on an ETL pipeline for a retail client, we noticed frequent data quality issues that caused downstream analytics to fail. T: My task was to identify the root cause and implement a robust solution. A: I introduced a data validation layer that checked for nulls, outliers, and schema mismatches before loading. I also set up automated alerts and scheduled re‑runs for failed batches. R: The pipeline’s error rate dropped by 70%, and the client could rely on real‑time dashboards without manual intervention. This experience taught me the importance of proactive monitoring and automated error handling in data engineering.