Define and automate data quality tests using YAML to validate row counts, null thresholds, and uniqueness across pipeline datasets.

Trace data anomalies through pipeline stages by analyzing logs and dashboards to identify and fix the exact source of failure.

Apply advanced Python debugging tools — including conditional breakpoints, watchpoints, and pdb — to diagnose and resolve pipeline issues.

Resolve complex concurrency bugs by reading stack traces and correlating thread logs to identify deadlocks and race conditions in code.