A third-party application Nesstar was used to publish indicators to their web site. However, the data ingestion process was entirely manual, leading to slow data refresh cycles and operational inefficiencies.
I was tasked with improving the reliability, speed, and accuracy of the data pipeline feeding into Nesstar. To address this, I self-initiated learning Java to reverse-engineer Nesstar’s backend processes, gaining a detailed understanding of its internal architecture. Leveraging this knowledge, I designed and implemented a custom automated data ingestion pipeline that integrated directly with the system’s internal structures, bypassing the manual upload interface. I managed the full project lifecycle — from system analysis and automation design through to deployment in a live production environment, ensuring minimal disruption to ongoing operations.
The automation reduced data loading times from several days to a few hours, dramatically improving data refresh rates and operational efficiency. It also freed up technical staff for more strategic work, minimized human error, and laid the groundwork for a more scalable and resilient data publishing process.