I’m an Analytics Engineer & Data Analyst with a solid knowledge of Data Science in FinTech.
My data journey started in 2020 while I was in my Master's program in Industrial Engineering and still didn’t know where I was headed. I enjoyed math and business, but no topic ever sparked any real passion in me. One evening, I watched the Netflix documentary about Cambridge Analytica and was completely blown away. I wanted to analyze data about people and find patterns. So, like every good data person, I of course skipped the basics of data and algorithms and dived straight into Data Science. After calculating survival predictions for Titanic passengers up and down with my first ML models, I still had no clue how to work with arrays or query data. During the pandemic, I used my time to immerse myself in all kinds of topics.
Finally, I had found an intrinsic motivation where "work" no longer felt like "work." And that motivation hasn’t changed since. Every line of code in my Git profile is something I taught myself, and I’m already excited for every new learning experience that comes my way.
- Languages: SQL, Python, Jinja (dbt)
- Tools & Platforms: dbt, Looker, Tableau, Dagster, Airflow, Mixpanel, Snowflake, Redshift, Google Analytics / Tag Manager
- Collaboration: Git, Confluence, MS Office
- Project Management: Jira, Click-up
I’m currently working on an end‑to‑end project where I’m building a complete ELT pipeline using dbt, Airflow/Dagster, and DuckDB to extract, enhance, and analyze my Strava and Whoop data with an LLM. I’m learning the concepts hands‑on and happily embracing all the little mistakes along the way.
