The Problem

We were drowning in spreadsheets. Every month, our finance team had to manually calculate payouts for over 200 publishers. This involved pulling data from three different ad platforms, reconciling it in Excel, and then manually initiating bank transfers.

It was a nightmare. It took three full days of work, errors were inevitable (and expensive), and our publishers were getting frustrated with late payments. We needed a way to scale without hiring an army of accountants.

The Solution

I built a custom Python application to automate the entire pipeline. I didn't just want to speed it up; I wanted to make it bulletproof.

Data Ingestion Wrote scripts to automatically fetch revenue reports via API from our ad partners every night, storing raw data in a SQL database.
Logic Engine Used Pandas to apply our complex revenue-share logic. This handled tiered commission structures and currency conversions automatically.
Payout Execution Generated a standardized batch file that could be uploaded directly to our bank portal, triggering hundreds of payments in one click.

My Role

I wore every hat on this one. I started by sitting down with the finance team to map out their manual process step-by-step (SOP creation). Then, I architected the database schema, wrote the Python code, and tested it against historical data to ensure accuracy.

The best part? Handing it over. The finance team went from dreading "payout week" to finishing the task before lunch on Monday. It freed them up to focus on strategy instead of data entry.

View Code on GitHub