"Narendra Mangala is a distinguished Cloud Data Engineering and Analytics professional with over 15 years of experience architecting and delivering enterprise-scale data solutions on Microsoft Azure and modern cloud platforms. Born with a passion for technology and problem-solving, he has built a career that spans the full spectrum of the data world — from database development and business intelligence to advanced cloud data engineering and AI-ready infrastructure. Throughout his career, Narendra has held impactful roles as a Senior Data Engineer, Data Architect, BI Developer, and Database Developer, accumulating deep expertise across industries and organizations. He is currently a Senior Data Engineer at Kenvue, where he plays a central role in building the Azure Data Lake Digital Core Platform — a flagship cloud initiative that centralizes data from multiple source systems and powers the company's digital transformation journey using Microsoft Azure and Databricks. Narendra's technical mastery spans Microsoft Fabric, Azure Data Factory, Databricks, PySpark, Scala, SQL, and Power BI. He is a recognized expert in Medallion Architecture, designing Bronze-Silver-Gold data pipelines that bring structure, reliability, and scalability to enterprise data ecosystems. His work in implementing Databricks Unity Catalog for centralized data governance, building CI/CD automation frameworks via Azure DevOps and GitHub Actions, and engineering real-time data quality monitoring systems has consistently elevated the organizations he has served. A forward-thinking innovator, Narendra has expanded his focus in recent years to the convergence of data engineering and artificial intelligence. His research portfolio — comprising 10 titles spanning 2021 to 2025 — explores transformative frontiers including agentic data pipelines, LLM-assisted ELT generation, generative BI with Power BI Copilot, and responsible AI data architecture with embedded GDPR compliance. Narendra holds multiple industry certifications, including Microsoft Fabric badges, Databricks Lakehouse Fundamentals, and credentials from Microsoft, IBM, and the University of Michigan. He has been recognized for his contributions with the Employee of the Quarter award and the HATS OFF Award for corporate training excellence."

AI, Azure Data Engineering AbstractAgentic Data Pipelines: Autonomous ELT Orchestration Using AI Agents on Microsoft Fabric and Databricks ELT workflows orchestrated by modern cloud services can be automated using agent-based frameworks that create and execute the required solutions. Agents working in a multi-agent fashion allow for tools such as Microsoft Fabric and Azure Data Factory to be used in novel ways. An evaluation of such an architecture, focused primarily on the agent-based approach within the Microsoft ecosystem, demonstrated that a complete pipeline could be processed as a single task on Databricks. Each agent’s specialization influenced not just the chosen tools but also the orchestration pattern. The decisions taken at these levels were logged as text and compared with human counterparts, providing insight about the explainability of the different patterns. Autonomous orchestration of ELT solutions using Microsoft Fabric Data Factory has proven successful and is paving the way toward an unassisted data engineering process. Support for these workers through user-defined tasks might be useful for more complex cases.