Maheswari Kunapareddy

Project: Monarch License Update (Cost Optimization, Licensing Compliance)

• Led the Monarch License Update, resulting in significant cost savings for the company.
• Collaborated with cross-functional teams to ensure seamless integration and compliance with regulatory requirements.

Project: Pay by Text (Customer Engagement, Payment Processing, Business Analytics)

• Developed and deployed Pay by Text, enabling the business to identify users not utilizing credit cards via short codes.
• Designed automated reports to provide insights into user payment behaviors and adoption trends.
• Managed and optimized ETL processes, ensuring high data integrity across databases. Automated metrics dashboards, reducing manual efforts by 70% and improving efficiency. This project is to improve customer experience by prompting the card members to pay before they incur late payment fees.
• Led the migration of Enterprise Data Platform (EDP) from MySQL to Snowflake, optimizing scalability and performance.

Project: Financial Reporting

• The Financial Reporting ETL takes about 3-4 hours to run each day. Based on the run times in test, it is expected to reduce the run time by 50 to 75 percent. This will take significant loads off the server whilst group B jobs are running allowing more timely access to the data by internal analysts
• Streamlined complex workflows by breaking them down into manageable components for easier implementation and maintenance.
• Optimized data processing by implementing efficient ETL pipelines and streamlining database design.
• Led end-to-end implementation of multiple high-impact projects from requirements gathering through deployment and post-launch support stages.

• Designed and implemented data pipelines using Azure Data Factory, Databricks, and Data Lake Analytics.
• Created ETL pipelines to extract, transform, and load data from various sources, enhancing efficiency.
• Utilized SQL scripts and PySpark to improve data analysis speed by 20%.

• Developed stored procedures, views, and triggers for efficient data processing and retrieval.
• Conducted ETL operations using Python, Spark SQL, S3, and Redshift, reducing data processing errors by 20%.
• Improved data reliability and processing speed through optimized transformation solutions.
• Collaborated on ETL (Extract, Transform, Load) tasks, maintaining data integrity and verifying pipeline stability.
• .Fine-tuned query performance and optimized database structures for faster, more accurate data retrieval and reporting.
• Enhanced data quality by performing thorough cleaning, validation, and transformation tasks.

• Executed data integration and ETL processes, ensuring seamless data flow and transformation.
• Developed batch and stream processing applications, improving processing efficiency by 40%.
• Optimized data storage strategies, reducing costs and enhancing query performance.