Sai Durga Karthik Nandiraju

• Remediate a security violation recognized by external auditors for a Kerberos based Java Spring boot application saving a potential $100,000 audit fine
• Develop API end points using FastAPI with internal teams ensuring seamless integration between frontend and backend systems
• Introduce read only cache strategy using Oracle Coherence reducing web application data load time by 10%
• Develop complex T-SQL stored procedure to identify financial products with similar legal entity, tenor and currency combinations required to fix front end errors improving trader profile processing time
• Automate financial product reports reducing report generation and email effort every Monday using pandas, Unix and T-SQL
• Enhance data visibility for better business decisions by adding additional trade data columns to Qlik view reports
• Create interactive dashboards and reports using tools like Power BI to effectively communicate complex data insights to stakeholders and management
• Migrate trader mandate application from legacy spring app to python reducing tech debt and thereby improving developer productivity

Client: Walgreens, IL, USA

• Developed SQL queries to analyze the customer sales data and build custom visualizations to reflect KPI’s using Tableau increasing customer retention by 20%
• Performed end to end testing of REST API’s covering all customer marketing journeys using Python and Postman
• Collaborated with internal stake holders to accomplish the migration of business-critical applications from On-Prem to Azure cloud saving $150,000 per year through 70% decrease in Hadoop server maintenance and scaling costs
• Debugged and fixed code errors on a Unix based raw data ingestion framework accounting to 10% defect decrease

Client: Vanguard, PA, USA

• Fixed the AWS glue job failures for financial reports, optimized python ETL code and ran CI/CD pipelines
• Optimized SQL query required to load marketing campaign data by accessing the CSV feed using partitioning and compression improving data load time
• Cleaned financial data to maintain accuracy and consistency, using data imputation and outlier detection using PySpark

• Created data visualizations in R studio using FMCSA data containing inspection data as part of exploratory data analysis
• Performed data processing, removed null values, duplicate rows and normalized the datasets using python NumPy and pandas
• Applied various statistical techniques, including regression analysis and hypothesis testing to uncover relationships within data
• Improved crash prediction accuracy to 70% by stacking logistic regression, random forest and KNN models