Manvitha Sirikonda

• Performed end-to-end validation across Bronze, Silver, and Gold Delta layers using SQL and PySpark, including source-to-target reconciliation and business rule validation.
• Optimized SQL queries and PySpark transformations, enhancing ETL performance and improving pipeline modularity and monitoring standards.
• Supported enterprise Financial Data Mart modernization by validating migrated datasets between Azure Synapse Analytics and Azure Databricks, ensuring accurate and reliable business reporting.
• Monitored Azure Data Factory production pipelines and resolved data discrepancies through root cause analysis of legacy iSeries data and ETL workflows.
• Led platform modernization initiatives focused on enhancing pipeline modularity, monitoring, error handling, and deployment standards.
• Leveraging Cursor AI extensively for AI-assisted development, debugging, refactoring, and bulk code modifications across hundreds of files.
• Utilizing Liquibase and standardized formatting rules to maintain consistent deployment and coding standards across enterprise workflows.

Client: Mars Incorporated (Fortune 500)

Project: Global Consumer Data Platform

• Designed and developed scalable Azure Data Factory and PySpark ETL pipelines processing Freight-to-Freight (F2F), Freight-to-Customer (F2C), Vendor, Labour Cost, Consumption, and Multi-Market datasets supporting analytics across 10 global markets.
• Architected parameterized, metadata-driven ETL frameworks using Azure Data Factory activities such as Lookup, ForEach, Copy Activity, Stored Procedures, and dynamic datasets to improve pipeline scalability and reusability.
• Processed 500GB+ enterprise data daily by optimizing Spark transformations, SQL queries, partitioning strategies, and Delta (Incremental) Loading.
• Built enterprise data warehouse solutions using Unity Catalog, Fact & Dimension modeling, and SCD Type 2 methodologies for downstream analytics and reporting.
• Automated SharePoint and OneDrive data ingestion into ADLS Gen2 using Power Automate and Logic Apps, streamlining data flow and minimizing manual tasks.
• Refactored Pandas-based pipelines to PySpark, enhancing scalability and maintainability of data processing workflows.
• Upgraded legacy codebase from L0 to L3 maturity by implementing modular, reusable design patterns and coding standards.

Client : Procter & Gamble

Project: Consumer Forecast Planning & Trade (CFTP)

• Developed Azure Data Factory and PySpark ETL pipelines supporting Consumer Forecast Planning & Trade (CFTP) business processes.
• Built scalable data ingestion and transformation pipelines integrating enterprise datasets into ADLS Gen2 and Azure SQL for downstream reporting.