Nasir

Mehmood

I worked on a project where I was tasked with automating the end-to-end processing of complex medical health data under strict budget constraints. I began by carefully designing pipelines, configuring blob storage, and setting up efficient triggers to build a reliable foundation. While the initial implementation was successful, it quickly became evident that optimization was crucial to manage long-term costs. I introduced scheduling improvements, reducing pipeline runs to five times a week, significantly cutting operational expenses. Further, by replacing repetitive loops with wildcard techniques and optimizing blob storage usage, I was able to streamline the system, cutting the number of pipelines and triggers by half. Although the redesign increased pipeline complexity—growing to 18 integrated functions—it resulted in a highly efficient, scalable, and cost-effective solution, delivering peak performance without compromising on reliability.