Data Engineer • Python Developer • Football Analytics
Professional trader turning a deep passion for data and football analytics into production-grade data engineering projects. I build end-to-end pipelines, interactive dashboards, and full-stack web applications — all driven by real-world data problems.
Background & motivation
I'm a professional trader with years of experience in the financial markets. While my day-to-day career is focused on trading, I have developed a strong passion for data engineering and software development — driven by a desire to solve real problems with code.
My projects reflect two things I care deeply about: football analytics and trading performance analysis. I've built production-grade data pipelines and interactive dashboards from scratch, and I also leverage AI coding agents to architect and ship full-stack applications — combining domain expertise with modern AI-assisted development workflows.
I'm looking for opportunities in Data Engineering or Python Development where I can apply my analytical mindset, self-taught programming skills, and relentless drive to learn and ship quality software.
Technologies I work with across my projects
Real-world applications built from scratch
End-to-end ELT pipeline for ingesting, modeling, and serving football statistics from the SofaScore API. Uses a medallion architecture (Bronze → Silver → Gold) orchestrated with Airflow, stored in MinIO/PostgreSQL, and transformed via dbt. Supports 20+ European leagues with both historical backfill and incremental daily updates. Produces 13 analytical mart tables for BI exploration.
A Streamlit-based web application for visualizing football analytics, team performance, match predictions, and league insights. Features multi-league support, interactive radar charts, head-to-head comparisons, form tracking, and league percentile rankings. Deployed live on Streamlit Cloud, connected to a PostgreSQL database populated by the data pipeline above.
A high-performance web dashboard for analyzing trading results, built entirely with AI coding agents. Features 16 key performance indicators, equity curves, calendar heatmaps, trade duration analysis, and an advanced tagging system. Demonstrates the ability to architect, direct, and ship a complete full-stack application by leveraging AI agents — from defining specifications and reviewing generated code to debugging and iterating on a production-quality result.
Consistent contributions across all projects
Interested in working together? Feel free to reach out.