I'm a passionate and results-driven Data Engineer with a strong background in building scalable data pipelines, performing insightful data analysis, and developing end-to-end machine learning solutions. I thrive on solving complex problems and turning data into actionable insights.
My experience spans across the modern data stack, including cloud platforms like Azure, and I'm always excited to learn and work with cutting-edge technologies in the MLOps and AI space.
Here's a glimpse of what I've been working on.
- Modern Data Stack Implementation (
-DE-modern-stack,data-engineering-dbt,end-to-end-data-engineering-W...): A series of projects where I designed and built end-to-end data pipelines using modern tools like dbt, Airflow, and Docker. These projects showcase data modeling, transformation, and orchestration. - Clorox Data Engineering Intern (
Clorox-data-engineering-intern): Contributed to enterprise-level data solutions during my internship. - Professional Contributions at Clorox (
clx-az-anaplan-repo): Worked on Azure-based data solutions, focusing on data archival, management, and ETL processes for financial systems like Essbase.
full-stack-mlops: Developed a complete MLOps pipeline, covering everything from data ingestion and model training to deployment and monitoring. A comprehensive project demonstrating skills in building production-ready ML systems.
Crime-analysis&Advanced-analysis-projects: Performed in-depth exploratory data analysis on various datasets to uncover trends and insights.- Collaborative Projects (
song-popularity-prediction,CarbonScorePrediction): Contributed to team-based data science projects, tackling challenges in prediction and classification.
- Apple MLX Framework (
mlx,mlx-lm): Explored and contributed to Apple's MLX, a new machine learning framework for Apple silicon. This involved working with both Python and C++. - LLM Hackathon (
llama_impact_hackathon-Rag...): Participated in a hackathon focused on Large Language Models, working on a project that implemented Retrieval-Augmented Generation (RAG).
- LinkedIn: [https://www.linkedin.com/in/pvrraju/]
- Email: [pvrraju9996@gmail.com]
Feel free to reach out if you'd like to collaborate on a project or just want to chat about data!