Headline
Technology Student | Platform Operations | Data & Analytics
About
Information Science Engineering student graduating in 2027 with hands-on experience in data analytics, Python, SQL, web applications and digital platforms. Interested in platform operations, user management, CRM, data quality, reporting and product operations. Experienced in building and testing technology projects, analyzing data, maintaining structured information and identifying technical issues. Strong attention to detail, problem-solving ability and willingness to learn new tools quickly. Interested in working at the intersection of technology, business operations and users.
Experience
Education
Projects
Cloud-Native Retail Data Engineering Platform
An end-to-end retail data engineering platform that automates data cleaning, transformation, validation, storage, and analytics. The pipeline processes raw retail sales data using Python and Pandas, stores structured data in SQLite through SQLAlchemy, and generates business insights using SQL analytics. Automated testing with Pytest and CI/CD using GitHub Actions ensure data quality and reliable pipeline execution.
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Adaptive Workload Orchestrator
Designed and deployed a distributed workload orchestration platform in Python and FastAPI, capable of scheduling tasks across 10+ simulated worker nodes with intelligent load balancing. Built a resource-aware scheduling engine using real-time CPU, memory, and queue metrics, reducing simulated task queue wait times by approximately 35%. Implemented heartbeat-based worker failure detection with automatic task reassignment, achieving fault tolerance with sub-5-second recovery time. Exposed REST APIs with average response latency under 60ms; built a monitoring dashboard tracking task lifecycle, worker health, and throughput analytics in real time.
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AI-Powered Code Evaluation System
Developed an automated code evaluation platform that analyzes programming submissions across correctness, complexity, and style dimensions using ML-based scoring models. Trained a classification model on 1,000+ code samples to predict code quality scores, achieving approximately 82% evaluation accuracy. Built a submission pipeline with real-time status tracking and automated performance reports, reducing manual evaluation time by an estimated 70%.
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