Hand-built Python automation that took bulk data work off Engineering

Problem

Merchants with successful affiliate programs but no dev team regularly needed bulk maintenance — moving a group of affiliates from one campaign to another, updating commission records, and similar. Through the platform UI that’s a one-by-one manual slog. At peak, these requests arrived 5–10 times a week and were landing on Engineering.

Finding

For the recurring bulk read/update operations I handled, documented scripts let Support complete the work through public API endpoints without escalating each request to Engineering.

Resolution

I built Python scripts that take a merchant-supplied CSV, validate it, target the appropriate API endpoint for the requested bulk operation, and emit a processing log on completion. The scripts are version-controlled in Git/GitHub and designed to run from support-staff machines using standard Python tooling. Safeguards: input is manually audited for malformed data at ingress and checked against the endpoint’s requirements before a run. The scripts validate input before each run and emit processing logs so failures can be identified and handled per record.

Impact

Requests that previously waited on Engineering are now completed within the working shift. I personally processed enterprise jobs of 8,000–15,000 records this way — and used those large runs to surface and report product defects that only appeared at scale, feeding real bugs back to the dev team.