Explain how to manage data handling, logging, and data reconciliation in ETL jobs.

Effective ETL pipelines require disciplined data handling, comprehensive logging, and rigorous reconciliation. Data handling involves extracting from heterogeneous sources, transforming with validation, cleansing, and enrichment, then loading into target systems. Use schema validation, type checks, and null handling to maintain quality. Logging should capture start/end times, record counts, error details, and performance metrics; structured logs (JSON) aid downstream analysis. For data reconciliation, compare source and target row counts, checksums, or hash digests to detect discrepancies. Implement incremental loads with change‑data capture (CDC) and maintain audit tables to track transformations. If mismatches occur, trigger alerts and rollback or re‑process affected batches. Automate these steps with workflow orchestrators (Airflow, Prefect) and store logs in centralized systems (ELK, Splunk). This ensures traceability, facilitates debugging, and guarantees data integrity across the pipeline.