A job succeeded, but the target table contains incomplete data. How would you identify the root cause?

When a job reports success but the target table has incomplete data, start by comparing source and target row counts and checksums. Use data profiling to spot missing or null values. Check the job logs for any warnings about truncated rows, timeouts, or partial commits. Verify that the transaction boundaries are correct—if the job uses batch inserts, ensure that each batch commits fully. Inspect the transformation logic for filters or joins that might drop rows unintentionally. Look for schema mismatches that could cause data to be discarded or defaulted. If the job writes to a partitioned table, confirm that all partitions were populated. Check for concurrency issues: another process might be truncating or deleting rows. Finally, run a small subset of the data through the pipeline manually to see if the issue reproduces, and add validation checks (e.g., row count assertions) to the job to catch such problems early.