Data Analyst with Master's in Information Management

Divya Dilip Ghorui

# *************@*****.*** 217-***-**** ï linkedin.com/in/divyaghorui + Chicago, IL Professional Summary

Data Analyst with a Master’s in Information Management and over four years of experience delivering scalable data solutions and production-grade distributed systems at Keelworks Foundation and Phoenix Technologies. Skilled in Python, SQL, Power BI, and cloud platforms like Azure and AWS, with expertise in building backend services using Spring Boot, Docker, and Kubernetes. Passionate about software architecture, automation, and scalable systems.

Experience

The Keelworks Foundation Data Analyst Jul 2024 – Present

• Spearheaded a scalable MDM system with SQL, Python, and Apache Airflow, automating data migration of 20M+ records with 99.9% accuracy in 2 months.

• Developed backend APIs and integration services with Azure OpenAI, RESTful APIs, and Docker, enabling AI-driven insurance process automation and reducing manual intervention by 40%.

• Optimized data pipelines by refactoring SQL queries and Python scripts, improving processing speed by 30%, and enhanced system reliability through containerization with Docker and orchestration using Kubernetes. Business Intelligence Group Senior Consultant Aug 2023 – Dec 2023

• Led development of a custom e-commerce loyalty platform for a Fortune 500 supply chain client, increasing user retention by 25% and driving over 10,000 repeat purchases within the first 3 months post-launch.

• Performed competitive market analysis on 15+ loyalty programs, identifying unique features that increased program par- ticipation by 17% and boosted monthly active users by 4,500.

• Advised on strategic API integrations and partnerships that expanded the loyalty program’s reach to 50,000 new customers over 4 months, enhancing cross-platform engagement. Phoenix Technologies, India Data Analyst May 2019 – Jul 2022

• Engineered ETL pipelines with Python (Pandas, SQLAlchemy) and SQL Server, automating complex financial data processing, cutting manual effort by 55% and reducing reporting time from 5 to 2 days.

• Designed and optimized backend data storage with PostgreSQL, crafting efficient schemas and SQL queries that improved query latency by 35%, enabling near real-time updates for investment dashboards.

• Built a scalable analytics service with multi-API ingestion, async Python processing, and REST endpoints, containerized with Docker and automated via cron, boosting data delivery from monthly to weekly and cutting manual effort by 90%. Technical Skills

Languages: Python, Java, C++, TypeScript, JavaScript, Swift, SQL, HTML, CSS, SCSS Backend: Spring Boot, FastAPI, Node.js, GraphQL, REST APIs, Kafka, Redis, WebSockets Frontend & UI: React.js, Next.js, Remix.js

Cloud Infrastructure & DevOps: AWS (S3, Lambda, EC2), Google Cloud Platform, Docker, Kubernetes, GitHub Actions, GitLab CI/CD, Jenkins, Terraform

Databases: PostgreSQL, MySQL, MongoDB, SQLite, BigQuery, Bigtable Data Analysis: Tableau, Power BI, Pandas, Scikit-learn, Matplotlib, Seaborn, PyTorch, TensorFlow Tools & Monitoring: Postman, Swagger/OpenAPI, Prometheus, Git, Linux, Agile Education

Master of Science in Information Management Aug 2022 – May 2024 University of Illinois, Urbana-Champaign

Course Work: Data Warehousing & Business Intelligence, Statistical Models, Database Design, Artificial Intelligence Projects

Crypto Token Market Insights Platform React, Python, CSS

• Developed an interactive dashboard in React and Python to track AAVE, DOT, and ETH, integrating real-time APIs and trend algorithms that improved recommendation accuracy to 85% and boosted simulated ROI by 25%. YouTube Trends Analysis System AWS S3, Lambda, Glue, Power BI

• Processed 320K+ records through a scalable data pipeline, cutting ETL time by 30% and serving aggregated insights via REST APIs for weekly interactive dashboards.

University Motor Pool Database Neo4j, DBMS, Graph Theory

• Designed and implemented a Neo4j-based graph database to optimize vehicle allocation and maintenance workflows, reducing query times by 21.4% and improving fleet usage efficiency by 12%. Brain Tumor Detection via Deep Learning VGG-16, Bee Colony, CNN

• Achieved 97.14% precision in tumor classification using a deep learning model combining VGG-16 CNN and Artificial Bee Colony optimization; published in the International Journal of Engineering Applied Science and Technology.