I specialize in developing scalable data solutions with expertise in Python, SQL, and cloud technologies. Currently seeking full-time opportunities in Data/Analytics Engineering.
I am committed to continuous learning and professional development
Validated expertise in cloud concepts and AWS services with a focus on security, architecture, and pricing.
Demonstrates ability to implement infrastructure as code using HashiCorp Terraform for reliable deployment.
I specialize in a range of technologies for data engineering and analytics
A selection of my recent data engineering and analytics projects.
End-to-end chess analytics platform that pulls games from the chess.com API and reveals patterns the platform doesn't show — tilt detection, session behavior, opening trends, opponent strength segmentation, and on-demand Stockfish engine evaluation with move-by-move replay. Built with a dbt medallion architecture, Dagster orchestration, and a 7-page Streamlit dashboard, tested across 9,000+ games.
Building a modern data pipeline to process Formula 1 racing data using real-time architecture. The system integrates Python for data collection, PostgreSQL for storage, Kafka and Debezium for streaming, and DuckDB with dbt for analytics.
Insights and tutorials on data engineering, analytics, and technology.
A deep dive into building ChessLens, an end-to-end chess analytics platform using dbt, DuckDB, Dagster, and Streamlit. Covers tilt detection, session analysis, opening trends, and on-demand Stockfish evaluation — turning 1,379 chess.com games into actionable insights.
In this article, I detail how I built the data collection layer for my F1 pipeline using Python-based collectors with async programming, robust error handling, and efficient connection pooling. I explain the challenges of collecting Formula 1 data and share lessons learned from implementing API integration and web scraping solutions.
In this article, I share my journey building a data pipeline for Formula 1 racing data using modern technologies. Learn how I leveraged PostgreSQL for storage, Kafka and Debezium for streaming, and DuckDB with dbt for analytics, implementing advanced patterns like CDC and SCD Type 2 along the way.
This tutorial explains how to build a content-based filtering system that predicts user ratings for movies based on their preferences. Learn the fundamentals of recommendation systems, from data preparation to similarity calculation and prediction, with practical Python implementation.
I'm open to new opportunities and collaborations in data analytics engineering. Let's connect and discuss how I can contribute to your team or project.