Hey, I’m Siva Prasath. I work at the intersection of data, scale, and real-world impact. With 3.5+ years in data engineering, I’ve spent my career turning messy, high-volume data into reliable systems that teams can actually trust and use. I’ve built data platforms that handle hundreds of millions to billions of records, powering everything from product analytics for global software platforms to finance pipelines that keep enterprise accounting systems running on time. My day-to-day work lives in the lakehouse world Databricks, PySpark, Delta Lake, and cloud-native pipelines across Azure and AWS. What drives me is not just moving data from point A to point B, but designing systems that scale cleanly. I enjoy breaking down monolithic pipelines, introducing incremental logic, enforcing data quality, and building datasets that are ready for analytics, dashboards, and machine learning without downstream chaos. Beyond tools and tech, I care about engineering discipline. Versioned pipelines, CI/CD for data, validation layers, and clear contracts between systems are things I take seriously. I’ve partnered closely with product managers, analysts, and data scientists to make sure what we build actually supports decisions, not just infrastructure. Outside of work, I enjoy deep-diving into real-world datasets, experimenting with performance optimizations, and continuously refining how large data systems should be built in production. If you’re looking to talk about data engineering, lakehouse design, pipeline scalability, or career growth in data, I’m always open to meaningful conversations.