Master's of Computer Science

United States

● Achieved the highest grade in courses Data Mining (with 97%) and Data Cleaning (with 109%) among all enrolled students, excelling in data transformation, outlier detection, feature engineering, and clustering.
● Presented a machine learning project on predictive modeling using Scikit-learn's KMeans implementation at the UIUC Big Data 2025 Conference, showcasing insights into user engagement patterns.
● Published a financial LLM research paper analyzing how LLM models like Bloomberg GPT are used in the industry.

Bachelors of Computer Science

Canada

● Graduated with High Distinction in the top 5% and placed on the Dean’s List of Scholars 2020-2023.

Software Engineer - Data Analytics

● Recognized for displaying business and system insights on a Tableau dashboard for stakeholders to grasp key metrics at a glance by streamlining data retrieval from Oracle’s database using Python and Shell Scripting.
● Optimized and wrote over 80 SQL queries for data extraction workflows using the Oracle Data Integrator API, resulting in a 35% runtime reduction of the data pipelines, and implemented parameterized interfaces for scalability.
● Automated key business processes by writing 200+ JavaScript files, of scheduled and map-reduce scripts consisting of invoice generation, receipt creation, and others resulting in 30% faster performance in NetSuite operations.
● Interconnected data integration by developing 73 NetSuite’s REST and SOAP API web services to external Power BI, Boomi, and Snowflake systems. This allowed system interoperability and facilitated data exchange with client teams

Software Testing & Automation Engineer

● Eliminated post-release issues through extensive testing of CLEO’s legal documentation software and from collaborative discussions with developers, using Python and Selenium for automating quality assurance.
● Coordinated the structure of CI/CD pipelines to include integration and regression testing of applications improving the release and developer feedback process by 70%.
● Designed and executed stress load tests using AWS CloudWatch and AWS Load Balancer by analyzing the system’s performance under high traffic conditions to reduce latency by 40% under peak workloads.
● Implemented test data management using Docker for isolated test environments and Kubernetes for orchestrating on-demand test data services, allowing consistent datasets to be used across pipelines.

Software Developer Intern

● Applied Apache Hadoop’s MapReduce framework to parallelize the processing of 8 terabytes of academic data, which reduced job completion times by 46% and significantly improved data accessibility for research teams.
● Developed C++ algorithms using cached memory pools for specific data transformation tasks, significantly minimizing computational overhead and reducing memory bottlenecks, which were causing performance regressions.

Virtualization, Docker, and Kubernetes for Data Engineering

Proficient in virtualization, containerization (specifically Docker), Dockerfile creation, multi-container orchestration with Compose and Airflow, Kubernetes core concepts, cluster architecture, deployment using cloud environments, GitHub Codespaces, and AI-driven tools, and effectively handle data scenarios through mastering containerization, deploying apps, and addressing production issues with cloud orchestration and SRE practices. https://www.coursera.org/account/accomplishments/verify/YLHA9M44CU55