Bansil Paghdal

Data Engineer with 2.5+ years of experience partnering with Fortune 500 clients to translate ambiguous business problems into clear, data-driven decisions. Experienced across the data engineering lifecycle, from designing scalable ETL/ELT pipelines and implementing Delta Lake architectures to developing data transformation workflows using dbt, optimizing SQL performance, and enforcing data quality standards. Combines strong technical expertise in Python, PySpark, SQL, dbt, AWS, and Azure with a focus on data governance and reliability to deliver measurable impact in fast-paced, Agile environments.

Associate (Data Engineering & Analytics) at Axtria (2024-07 – Present)

• Architected and built ETL/ELT pipelines using Azure Data Factory, Azure Data Lake Storage Gen2 (ADLS), Databricks Delta Lake and PySpark for pharmaceutical client data, processing 10M+ records daily with 80-95% improvement in execution time through incremental load patterns and partitioning strategies.
• Automated pipeline scheduling and Maintained pipeline code in Git with structured branching, peer code reviews, and CI/CD-based automated testing and deployment, ensuring consistent, production-safe releases across Azure Data Factory and Databricks.
• Optimized 15+ complex analytical SQL queries and stored procedures using advanced techniques (CTEs, window functions, query plan analysis), achieving 45% performance improvement on datasets exceeding 10M rows and reducing table scans by 60%.
• Built observability into pipeline operations through automated anomaly detection and SLA-based failure alerting, cutting failure response time by 40% and reducing downstream data errors by 20%.
• Implemented lineage-aware data validation checks at eachc stage during a CRM-to-Databricks migration, ensuring traceability and 100% consistency across 5M+ records.

Analyst Intern at Axtria (2024-01 – 2024-07)

• Streamlined data pipelines for a pharmaceutical client by supporting Azure-based ETL workflows, improving data availability for reporting and analytics teams.
• Automated data validation and quality assurance processes using Python (Pandas, NumPy) and SQL, building reusable frameworks that reduced manual QA effort by 70% and improved data accuracy metrics.
• Developed and maintained dbt-based ELT pipelines to transform raw data into scalable analytical data marts, implementing incremental models, data quality tests, reusable macros, and automated documentation.

Integrated B.Tech and M.Tech in Information Technology – Indian Institute of Information Technology and Management (2019-07 – 2024-05)