• Successfully migrated a legacy lounge pass management system for United Airlines, transferring ~21 million OneTimePass records from an SQL database to AWS DynamoDB, ensuring a seamless transition with zero data loss.
• Designed optimized database tables in AWS DynamoDB and implemented scalable web APIs using C# and .NET, enhancing system functionality, data retrieval, and integration with AWS infrastructure.
• Extensively worked with AWS services, including DynamoDB for storage, AWS Lambda for serverless workflows, AWS Parameter Store for secure configuration management, and AWS Elastic BeanStalk for deploying and managing containerized applications, ensuring scalability and performance.
• Collaborated with DevOps engineers to deploy the application securely and efficiently using Veracode for security scanning and Harness pipelines for CI /CD automation, delivering high-quality releases.
• Leveraged Docker to containerize the application, ensuring consistent development workflows across environments and enabling streamlined deployment processes.
• Partnered with testers to identify, debug, and resolve application issues, guaranteeing high-quality releases and a seamless user experience.

Tools & Technologies: AWS DynamoDB, AWS Lambda, AWS Parameter Store, AWS EKS, .NET C#, SQL, Docker, Kubernetes, Veracode, Harness, Postman, Swagger/OpenAPI, boto3, Git

• Fetched and analyzed sensor data stored in AWS RDS databases, accessed through MySQL Workbench, containing measurements from devices such as room usage, device activity, weather conditions, and various other sensor inputs.
• Designed and developed machine learning models in R and Python to predict energy savings based on usage patterns and room occupancy, delivering actionable insights for optimizing energy consumption.
• Performed statistical analysis on the sensor data to uncover trends and correlations, contributing to data-driven decision-making processes.
• Created and visualized data insights using Tableau, designing analytical dashboards with interactive charts and tables for stakeholders to easily interpret complex data.

Tools & Technologies: MySQL, Jupyter Notebook, SQL Workbench, Tableau, R Studio

• Analyzed customer data for Ezeelo, an online consumer retail service with over 20,000 customers worldwide, providing insights into product demand across different districts and predicting inventory requirements for various countries.
• Researched and implemented a scalable machine learning algorithm to recommend similar products to users, increasing product sales by 8% through targeted recommendations.
• Normalized large datasets using SQL and Python to extract relevant insights, and performed detailed statistical analysis using R Studio to identify trends and forecast inventory needs.
• Visualized customer behavior, sales trends, and pricing insights using Tableau and ggplot2, creating interactive dashboards and detailed visualizations to assist stakeholders in data-driven decision-making.
• Delivered actionable insights into product demand, enabling the company to optimize inventory distribution and enhance customer satisfaction across diverse regions.

Tools & Technologies: MySQL, Jupyter Notebook, Tableau, R Studio