

Rachana is a data engineer with 5+ years of experience with a deep passion for how businesses leverage data, focusing on building end-to-end data systems, from ingestion and pipelines to analytics. She is skilled in PySpark, SQL, Python, and Snowflake, focused on turning data into reliable, real-world business value.
Rachana talks about building scalable data engineering systems that enable modern data products, cloud-native architectures, AI-ready infrastructure, and reliable enterprise-wide analytics operations.
Rachana Medishetti represents a new generation of data engineers: professionals who are not just moving data, but engineering the operational backbone that modern AI-native enterprises depend on. As a Senior Data Engineer at The Modern Data Company, she contributes to scalable data ecosystems built on interoperability, reliability, and product-driven architecture, closely aligned with the vision behind DataOS, the company's data operating system for unifying fragmented data environments into governed, intelligent systems at scale.
Her engineering approach is rooted in understanding how enterprise data behaves across distributed and cloud-native environments, focusing on building reusable data foundations that improve accessibility, reliability, and operational efficiency. Through Modern Data 101, she also shares practical perspectives on data engineering, scalable architectures, and the evolving role of data platforms in enabling AI-ready enterprises, bringing grounded, implementation-focused insight to conversations around data transformation.
Before joining The Modern Data Company, Rachana spent over five years at Tavant, working on enterprise-scale data engineering initiatives using Hadoop, Azure Data Factory, and modern cloud infrastructure, gaining hands-on experience across both legacy and modern data ecosystems. What distinguishes her work is the ability to combine engineering precision with platform-oriented thinking, viewing modern data systems as strategic enablers of intelligence, scalability, and AI-driven innovation, reflecting the broader shift from fragmented pipelines to intelligent, platform-centric data foundations.