About
I learn systems by building them.
I’m a data engineer focused on reliable data platforms—and a hands-on learner who likes to turn technical questions into measurable experiments.
Data engineering is the center of my work.
My day-to-day technical world includes Databricks, PySpark, ETL, Azure, and Microsoft Fabric. I’m interested in the full path from raw data to a platform that people can trust and use.
Outside that core work, I explore local AI and hardware performance. My current lab work uses llama.cpp on Windows to compare model sizes, quantization, context lengths, GPU offloading, throughput, and system resource use.
What guides the work
- Understand the tradeoff, not just the recommended setting.
- Measure behavior on real hardware before drawing conclusions.
- Document the path so the result can be reproduced later.
- Prefer clear systems that are easier to operate and improve.