Saurav Chakraborty

Azure Data Engineer with 4+ years of experience architecting and optimizing large-scale ETL/ELT pipelines using Azure Databricks, PySpark, Spark SQL, and Azure Data Factory. Proven track record of improving pipeline performance by up to 40% and reducing processing time by 30% through efficient data partitioning, query tuning, and workflow optimization. Skilled in integrating SAP S/4HANA and ERP data into Azure Data Lake and Azure SQL ecosystems, implementing data governance via Unity Catalog, and delivering reliable, audit-ready data solutions for enterprise analytics.

Recognized for technical excellence with multiple internal performance awards.

Senior Data Engineer - LTIMindtree (LTM)

(2026-03)

• Architect scalable data processing pipelines on Azure Databricks and PySpark, and orchestrate large-scale ingestion/transformation workflows in Azure Data Factory for enterprise data platforms.
• Build PySpark and Spark SQL transformation logic — joins, aggregations, and cleansing — to process structured and semistructured datasets in ADLS Gen2 for analytical workloads.
• Design data lake storage structures and partitioning strategies that improve query performance and reduce data retrieval latency.
• Partner with data architects and analytics stakeholders to deliver reusable, scalable data engineering solutions aligned to business requirements.
• Establish version control and CI/CD practices using Git and Azure DevOps, streamlining pipeline deployment and reducing release errors.
• Lead internal solution accelerators and proof-of-concept initiatives advancing the team's Azure data engineering capabilities.

Azure Data Engineer - Tata Consultancy Services (TCS)

(2023-01 - 2026-02)

• Designed and delivered end-to-end ETL pipelines using Azure Data Factory, Azure Databricks, PySpark, and Spark SQL for large-scale enterprise data processing initiatives.
• Built batch and streaming pipelines to ingest data from SAP S/4HANA, ERP, and OMP systems into Azure Data Lake Gen2 and Azure SQL Database, enabling unified enterprise reporting.
• Processed and optimized planned order and scheduling data, improving accuracy of product delivery planning and operational reporting.
• Optimized Databricks ETL workflows and Spark jobs, achieving a 30% reduction in processing time and improved system throughput.
• Engineered scalable storage solutions using Azure Blob Storage, ADLS Gen2, and Azure SQL, delivering a 40% improvement in data retrieval time.
• Implemented Databricks Unity Catalog to strengthen data governance, access control, and regulatory compliance across multiple data products.
• Built and maintained CI/CD pipelines in Azure DevOps, and performed end-to-end data validation to ensure data accuracy and reliability.

B.Tech - Information Technology - Government College of Engineering and Leather Technology (2018 - 2022)

Higher Secondary - Nimta Higher Secondary School (2016 - 2017)