Lokeshkumar Madabathula is a Senior Data Engineer with over 15 years of experience in enterprise data engineering, cloud-native architectures, artificial intelligence, machine learning enablement, metadata-driven data platforms, and large-scale analytics. He has successfully designed and implemented enterprise-scale data solutions for global organizations across healthcare, finance, and technology sectors, specializing in scalable data pipelines, cloud data platforms, enterprise lakehouse architectures, data governance, and intelligent analytics.

He actively contributes to the global research community as a peer reviewer for numerous Scopus-indexed journals and international conferences and serves as a Technical Program Committee member, Session Chair, conference reviewer, and hackathon judge. His research interests include Enterprise Lakehouse Architectures, Metadata-Orchestrated Data Systems, AI-Driven Data Engineering, Cloud Computing, Machine Learning, Autonomous Data Reliability, Intelligent Enterprise Platforms, and Cloud Analytics. He is also the author of multiple research papers and intellectual property contributions in enterprise data platforms and intelligent cloud architectures.

Title: 

Building Intelligent Enterprise Data Platforms: Metadata-Driven Architecture for Scalable Cloud Analytics - 

Abstract:

 This presentation explores how metadata-driven enterprise data platforms can transform traditional data engineering into intelligent, scalable, and cloud-native analytics ecosystems. It discusses architectural best practices for designing metadata-orchestrated data pipelines, automated governance, real-time monitoring, and AI-enabled analytics that support enterprise-scale decision-making. The session also highlights practical implementation strategies for improving scalability, operational efficiency, data quality, and cloud-native analytics while laying the foundation for autonomous data engineering platforms.