Mr. Dinesh Eswararaj

The AI-Native Data Engineer: Skills, Tools & Architecture for the Next Decade

Abstract:

The role of the data engineer is undergoing a profound transformation. With AI redefining how data is processed, governed, and activated, the next generation of data engineers must evolve from pipeline builders into designers of intelligent, self-optimizing data ecosystems. This keynote explores what it means to be an AI-Native Data Engineer- one who blends traditional data engineering expertise with AI-driven automation, cloud intelligence, and metadata-first design principles.

This session will unpack the skills, tools, and architectural patterns that will shape the next decade of data engineering. Attendees will learn how AI can accelerate development, automate quality checks, optimize performance, and govern data at scale across platforms such as Microsoft Fabric, Azure, Databricks, and modern Lake House architectures. The talk highlights how LLM-assisted development, intelligent orchestration, and active metadata can eliminate repetitive work, reduce technical debt, and enable engineers to focus on higher-value innovation.

Participants will leave with a clear roadmap to evolve into AI-native data leaders equipped to build autonomous data platforms, harness AI responsibly, and future-proof their careers in a rapidly changing data landscape.

Profile:

Dinesh Eswararaj is an accomplished Data Architect and Lead Data Engineer with over 20 years of experience driving large-scale data modernization, cloud transformation, and automation initiatives across global enterprises. He specializes in architecting cloud-native data platforms, building metadata-driven frameworks, and enabling AI-powered data engineering capabilities that significantly reduce cost and accelerate time-to-insight. His expertise spans Azure, AWS, Microsoft Fabric, and modern Lakehouse architectures, with a proven track record of leading end-to-end migrations, self-healing pipelines, and intelligent data quality frameworks. He has published multiple research papers on data engineering, AI/ML adoption, DevOps, and legacy modernization, and actively contributes to the tech community through articles, thought leadership, and conference participation.