Optimizing Workflows to Improve Data Reliability and Scale
At Amazon, I focused on improving operational workflows to make data more reliable and accessible for teams. I built automated systems using Python, SQL, and AI-assisted development to improve data accuracy across 300+ projects and reduce manual auditing. I also designed internal tools to surface performance insights, enabling teams to identify what was working and prioritize high-impact work.
Company
Amazon
Date
May - August 2025
Scope
01
The Problem
I realized the issue wasn't bad data… it was the process creating it.
Engineers were fixing the same mistakes over and over instead of preventing them at the source.
Before writing any code, I wanted to understand what was making everyone's work harder than it needed to be.
02
My Approach
I traced the problem back to where information first started breaking down.
I interviewed engineers and program managers daily to understand how they actually worked.
I built tools that fit existing workflows, rather than asking people to change how they worked.
03
Key Takeaway
One thing I'll take with me from Amazon is that small improvements add up. A workflow that saves 1 person 5 minutes can save hundreds of hours down the line.
Every spreadsheet, dashboard, or automation is really a product. If it makes someone's day a little easier, it's worth building well.
