A data and software-focused engineer ,Master's in Data Science @ UMass Dartmouth
I build reliable, scalable systems that solve real-world problems and create measurable impact. My work lives at the intersection of data, backend engineering, and analytics, where I turn complex datasets into production-ready systems that enable better decision-making, automation, and customer experiences.
I’ve delivered end-to-end solutions across multiple domains — from backend data pipelines and APIs to experimentation frameworks and analytical services powering sentiment analysis, recommendation systems, and workflow automation. I enjoy owning problems from design through implementation, with a strong focus on performance, correctness, and scalability.
I’m currently open to opportunities in Backend Data analytics, Data Engineering, Data Science and Software Engineering, and I enjoy collaborating on impactful projects, exchanging ideas, and building data-driven, engineering-led products.
Always excited to collaborate on data-driven products and scalable engineering 🚀
🔹 Amazon (BI & Data Engineer Intern)
✅ Architected automated content curation platform for 1M+ title catalog across Kindle marketplaces
✅ Reduced pipeline runtime by 5 minutes using CTEs, window functions, broadcast joins
✅ Built QuickSight dashboards cutting stakeholder decision time by 60%
✅ ETL pipelines processing 200M records in <30min via Redshift/S3 optimization
🔹 Deloitte (Software Engineer)
✅ Python anomaly detection reducing manual review from 100→8 hours
✅ SQL A/B testing achieving F1-score 0.82 for sentiment analysis validation
✅ User analytics platform for 80K users, boosting engagement 30%
🔹 Solenis (Data Engineer)
✅ Automated ETL migration achieving 90% data accuracy in 2 months
✅ Tableau dashboards monitoring $150M revenue for 100K+ customers
✅ Reduced processing time 60% via Azure Data Factory & stored procedures
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FitGen AI (Google Hackathon Winner)
Real-time workout form analysis using Gemini-1.5-Pro on GCP. Dockerized with Streamlit, Cloud Run, OpenAI integration.
[Code] -
Microsoft LLM Lingua Prompt Compression
Reduced token size 70%, saved 55% compute using RAG, vector embeddings, ChatGPT.
[Code] -
Alexa Sentiment Analysis (97% accuracy)
Ensemble ML (XGBoost, Random Forest) on Amazon reviews with real-time dashboards.
[Code] -
END-to-END-RAG-PROJECT End-to-end Retrieval-Augmented application by LangChain , OpenAI API, and hugging face embeddings [Code]
graph TB
SQL[SQL<br/>CTEs - Window Functions<br/>Optimization] --> Data[Data Engineering<br/>Redshift - S3 - Azure DF]
Python[Python<br/>pandas - PySpark<br/>FastAPI] --> ML[ML<br/>XGBoost - PyTorch<br/>LLMs - RAG]
BI[QuickSight - Tableau<br/>Power BI] --> Backend[APIs<br/>Microservices]