Data engineer in the Netherlands. Notes on data pipelines, AI engineering, and the trade-offs behind them.
NL Transport Pulse: an End-to-End Data Pipeline for Dutch Railway Reliability
The Netherlands runs one of the densest rail networks in Europe, with 20+ operators sharing the tracks. Delays are a daily experience, but as a passenger you never learn why: weather, one bad operator, a corridor bottleneck, or a disruption cascading through the network. NL Transport Pulse is my attempt to answer that with data: a pipeline that ingests 70M+ train departures (2019 to 2026), 15 years of disruption records, and daily weather observations, transforms them through a medallion architecture on GCP, and serves an interactive dashboard for corridor-level reliability analysis. It started as my capstone for the DataTalksClub DE Zoomcamp 2026. ...
Building an AI Tool for an AI Skeptic
My partner is a biocatalysis researcher. I am a data engineer who reaches for LLMs daily and mostly likes what I find. She keeps her distance from all of it, on solid professional grounds: experimental science runs on reproducibility, verifiability, and traceability, and raw LLM output guarantees none of the three. To be precise about the skepticism: she had never actually used an agent, a workflow, or a data pipeline. The distance came from the failure mode everyone has heard about, hallucination. For a scientist whose day job is separating verified claims from plausible ones, a tool famous for confident fabrication starts in deep debt. ...