Kshitij Khandelwal

1. Developed and maintained automated ETL pipelines using DBT, integrating data from AWS S3 and orchestrating workflows with Apache Airflow to ensure efficient and reliable data processing.

2. Achieved up to 90% improvement in time complexity and query performance by migrating legacy Java and Scala data processing code to highly optimized DBT models.

• Designed and implemented a scalable data warehouse using a star schema in SQL Server, and orchestrated 20+ automated ETL workflows with Azure Data Factory to support multi-country market share analysis for Hershey’s across 10+ regions.

• Built metadata-driven ETL pipelines using ADF and PySpark in Azure Databricks to process millions of sales records across global markets.

• Pipeline Optimization: Reduced pipeline failures by 25% across cross domain datasets (Finance, Procurement, Sales) by leveraging Azure Data Factory, optimizing SQL queries, and implementing error handling mechanisms.
• Machine Learning Implementation: Developed algorithms using Pearson correlation and IQR-based outlier detection on the Toyota dataset, boosting data quality by 35%.

• Provided end-to-end support for Azure Data Factory (ADF) pipelines, including monitoring, debugging, and performance optimization.
• Automated data workflows and implemented error-handling & alerting mechanisms to ensure high availability.
• Assisted clients in troubleshooting failed pipeline runs, identifying root causes, and applying fixes.

Completed a Bachelor of Engineering in Computer Science with notable coursework in Data Structures Algorithms, Cloud Computing, and Database Management.