Anjan User

Designed automated pipelines using PySpark and Azure Data Factory on Databricks, reducing manual workflows and cutting data processing time by 30%. Leveraged Delta Lake on Azure Data Lake Storage to implement ACID-compliant, versioned data pipelines, enabling reliable time travel queries, schema enforcement, and efficient updates on massive datasets. Built real-time analytics pipelines using Azure

Collaborated with client stakeholders, business analysts, and internal consultants to define data acquisition strategies, transformation workflows, and pipeline architecture for large-scale digital transformation initiatives across finance and public sector clients. Designed and implemented scalable ETL pipelines using Azure Data Factory and Azure Databricks to ingest, clean, and transform high-vo

Built a cloud-native analytics platform on Azure, enhancing data accessibility for 100+ stakeholders and improving reporting speed by 45% through real-time pipelines and Power BI integration. Optimized production-scale ADF and Databricks pipelines with custom partitioning strategies and load balancing, ensuring seamless execution of high-volume data loads in enterprise reporting systems. Engineere

Assisted in cleansing and transforming multi-format source data (Excel, APIs, CSV) using SQL and SSIS, ensuring smooth ingestion into Azure Data Lake and operational databases. Created interactive Power BI dashboards to visualize regional automotive sales, leveraging DAX, dynamic filters, drill-through reports, and calculated columns to drive actionable insights. Ingested structured and semi-struc

Completed a Master of Science in Computer Science and Engineering. Focused on advanced topics in the field, enhancing skills in data engineering and related technologies.

Obtained a Bachelor of Technology in Computer Science Engineering. Gained foundational knowledge in computer science principles and engineering practices.