Data Engineer based in Kigali, Rwanda. Kafka, Spark, and Django, turning raw event streams into systems people can trust.
I came into data engineering through network operations: watching KPIs and alarms across live infrastructure before I ever built a pipeline of my own. That instinct still shapes how I build today, systems that degrade gracefully, and tell you when something's wrong before a user does.
Two of the systems I'm most glad exist. More on the projects page.
An end-to-end pipeline that simulates live transaction traffic and scores it in-flight. Predictions split two ways: Postgres for a low-latency Streamlit dashboard, HDFS as a data lake for batch analytics and retraining.
Compares Ookla's self-selected speed tests against RURA's full subscriber census, then models the probability a location enters the crowdsourced sample to correct for it. Thirty quarters of measurements, terrain, and technology mix pulled from 28 regulator PDFs feed an out-of-fold calibration step.
All projects, including applied systems and smaller experiments