SANTHOSH KUMAR GADIPALLY

Project/Client: The Clorox Company

Role: Packaged App Development Analyst

• Developed end-to-end ETL/ELT pipelines using Azure Data Factory (ADF), Azure Data Lake Storage (ADLS), Azure SQL, and Azure Databricks, implementing incremental loads, change detection, and hash-based upsert logic.
• Built and maintained data ingestion and transformation pipelines integrating SFTP, Snowflake, and SAP OpenHub, leveraging ADLS and Delta Lake with data validation and quality checks.
• Designed scalable, metadata-driven ingestion pipelines for 950+ tables, supporting schema evolution, dynamic table configurations, and automated data validation across the Azure data platform.
• Managed production ADF pipelines, Databricks jobs, and Delta tables; troubleshot data load failures, pipeline execution errors, and data quality issues, tracking and resolving incidents through ServiceNow.
• Optimized complex SQL queries and Denodo views by consolidating multiple views into streamlined integration and reporting layers, improving query performance and maintainability.
• Collaborated with clients and cross-functional teams to translate business requirements into technical solutions.
• Deployed Databricks code through GitHub Pull Requests and CI/CD pipelines, performing code reviews and release approvals to ensure controlled production releases.

Project/Client: CUMMINS

Role: Junior Data Analyst

• Developed and supported Azure Data Factory (ADF) ETL pipelines to integrate sales and customer data from multiple sources into a centralized data warehouse for reporting and analytics.
• Developed and optimized SQL queries to analyze sales performance across regions, products, and time periods, identifying trends and business opportunities.
• Collaborated with business analysts and data engineers to gather data requirements and deliver solutions aligned with business objectives.
• Troubleshot data ingestion and storage issues, improving data accuracy, integrity, and pipeline reliability.
• Designed and optimized relational database schemas based on business requirements, supporting efficient data storage and retrieval.