Over the years, duplicate member accounts had piled up - thousands of them. Different systems, different entry points, no single source of truth. The Data and Membership teams were facing weeks, possibly months, of manual work to clean it all up.
I worked with both teams to build an automated merge process. Our data analyst brought a fresh approach, using Python scripts to clean data and identify matches before we even got to the merge logic. We identified match criteria, wrote the logic to handle edge cases and automatic matches (no interaction necessary), and ran it in stages so staff could verify results along the way. What would have taken one or two people months of tedious, error-prone work got done in a fraction of the time - with better accuracy.
These types of projects give me the most pride as I look back on them. Listening to co-workers in other departments. Taking a painful manual process, understanding the rules behind it, automating the boring parts, and giving people their time back to do work that actually matters.