🔭 I’m currently working on
Building real-world data pipelines using Apache Airflow, PySpark, and PostgreSQL — basically turning chaos into insight, one DAG at a time. Focused on making things scalable, performant, and slightly less likely to crash at 3 AM.
🧑🤝🧑 I’m looking to collaborate on
Open-source projects that involve Data Engineering, ETL automation, or any excuse to use the phrase “modern data stack” unironically.
🤝 I’m looking for help with
Taming the PySpark beast (performance tuning is my Everest), writing SQL so clean it cries, and figuring out how to not break things while building real-time data pipelines.
🌱 I’m currently learning
PySpark (I’m still in the “Googling every error” phase)
Airflow DAG structuring best practices (read: making DAGs that don’t resemble spaghetti)
Cloud data platforms like AWS (S3, Glue, Redshift) and GCP BigQuery — aka “The Cloud™”
💬 Ask me about
How I went from "What even is a data pipeline?" to "Let’s automate all the things!"
Hands-on learning strategies for aspiring data engineers
Debugging SQL queries like a crime scene investigator
⚡ Fun fact
I reverse-engineer open-source data tools just for fun. Also, I believe SQL is a full-fledged programming language and should get the respect it deserves (fight me).
