I spent 4 years working as a Data Scientist at Sony PlayStation. My main job was managing and analyzing their service data. It was a long project that needed both technical skills and smart planning to build solutions that could handle the company's growth.

Sony PlayStation's internal teams had a real problem with their data management system. They were processing over 500,000 invoices, but the old ETL pipeline was incredibly slow. Teams that needed data to make decisions were stuck waiting. The system just couldn't handle the amount of data coming in, and the delays were affecting everyone's work.

I completely rewrote the ETL pipeline using Java. The old system had to go. The new version was 120 times faster. It handled all 500,000+ invoices without breaking a sweat.

I built interactive graphs to make the data easier to understand. I also created custom web applications for different teams based on what they actually needed. Then I put together dashboards that showed real-time insights.

I spent a lot of time with the internal teams to figure out what they really needed. I made sure everything worked with their existing setup. And I kept updating things based on their feedback.

There were a few big challenges here.

Scale was the first one. When you're dealing with hundreds of thousands of invoices, you need to think carefully about how you structure everything.

Performance was another issue. The original system was so slow that I had to redesign the whole thing from scratch.

Integration meant making sure the new tools didn't break anything that was already working.

Managing stakeholders across multiple teams was tricky. Everyone had different needs and priorities.

Long-term maintenance mattered too. I wanted to build something that would last and could change as the business changed.

I worked with Java for programming. Built and optimized the ETL pipeline. Did custom web development. Created data visualizations and interactive graphs. Put together business intelligence dashboards. Processed and analyzed large amounts of data.