Turning complex data into simple, reliable systems that drive real impact. I build scalable solutions, uncover insights, and bring ambitious ideas to life through engineering, collaboration, and problem-solving.
I’m a Melbourne-based Data and AI Engineer who loves building technology that actually makes life better. My focus is on designing scalable, end-to-end systems that are robust under the hood yet effortless for the people who rely on them.
What drives me is impact. I’m particularly drawn to projects that strengthen society’s trust in technology - systems that are secure, transparent, and reliable. Within that, I’m especially interested in healthcare and large-scale consumer platforms, where trustworthy data systems and intelligent automation can improve outcomes for millions of people. I get excited about building real-time, automated solutions that cut out repetitive work, enable faster decisions, and give teams the tools to focus on what truly matters.
I thrive in structured environments where collaboration, clear processes, and engineering discipline come together to deliver results. At the same time, I bring curiosity, creativity, and a problem-solving mindset - because the best systems don’t just run; they evolve and adapt to what people really need.
If you’re looking for someone who combines strong technical depth with a genuine motivation to create meaningful impact, I’d love to contribute that energy to your team.
Sydney, Australia • Apr 2024 – Jan 2025
Seoul, South Korea • May 2021 – Apr 2022
University of Technology Sydney (UTS) - Graduated with Distinction
GPA 6.44 • WAM 84.56
University of Economics in Katowice
Building a real-time data pipeline with Kafka, ksqlDB, and Avro;
landed Parquet to S3, queried via Athena, transformed with dbt,
orchestrated in Airflow, served APIs through FastAPI, and
visualized insights in Metabase, with CI/CD on GitHub Actions.
Designed and deployed automated ETL pipelines with built-in validation checks and dynamic summary tables, streamlining reporting and eliminating manual effort.
Award-winning app for coffee lovers, which I led the development of. Designed with MVVM architecture for clean, maintainable code, featuring a polished interface and seamless user experience.
Deep learning web app for skin cancer detection, using CNN and U-Net for classification and segmentation. Built with Streamlit for an intuitive interface and rapid, accurate results.
Lightweight game project; demonstrates clean code and simple mechanics.
Built scalable web scraping systems with authentication handling and dynamic pagination, delivering clean, API-ready datasets for high-impact analytics.
Academic project: using FinBERT sentiment analysis to predict stock price direction (UTS coursework).
Responsive front-end website demonstrating clean UI design.
Swift calculator app built with test-driven development, passing all unit tests.
Academic project highlighting data handling and software structure.
Based in Melbourne, VIC • Open to Data Engineer, Data Scientist, Data Analyst, AI Engineer, or Software Developer Roles