I bring sources together into a modelled, tested data warehouse: pipelines that run every day, figures that are the same for every department, and reporting nobody has to assemble by hand anymore.
That's the work I did as a data engineer at the Netherlands Aerospace Centre and as a data analyst at Politie Noord-Holland — two environments where a wrong figure has real consequences. The projects on this site show the same approach, with the code in the open.
What a team can expect from me
01
Model before building
Grain and dimensions on paper first, SQL second. A documented star schema stays understandable after I've moved on.
02
Tests on every change
Not-null, uniqueness and relationships as tests in the pipeline, with CI running them on every pull request. A mistake surfaces before it reaches a dashboard.
03
Batch and streaming
Daily ELT where that's enough, Kafka and windows where figures have to be right within minutes — including a dead-letter queue and replay for the failure path.
04
Explainable to the business
A warehouse isn't finished until the people using it understand it. Documentation and handover are part of the work, not an afterthought.
Where I did this
2024
Data Analyst
Politie Noord-Holland
Data analysis and automation within criminal law, under hard privacy and information-security requirements. Pipelines where privacy by design is the starting point, not an appendix.
2023
Data Engineer
Netherlands Aerospace Centre (NLR)
A wide range of data projects in a research environment where the figures get checked — and where the pipelines behind them have to hold up to that.
Since 2020
Instructor
Udemy
Courses on data and technology for people without a technical background: 241 students with a 4.2 rating. The same skill a team needs at every handover.
One definition per figure, the same for every department.
Mistakes surface in the pipeline, not in the dashboard.
Everything reproducible: from zero with one command.
Projects that show it
Two own projects, from raw source to working result — with the full approach and the code in the open.
Open data warehouse: a Kimball star schema on RDW and CBS
End-to-end ELT platform on Dutch open data: Python ingestion, dbt transformations and a documented star schema — with tests and CI on every pull request.
Real-time public transport streaming pipeline on Kafka
Every vehicle in Dutch public transport, streamed live — from GTFS-realtime feed to a map of the whole country, with the correctness guarantees to go with it: per-partition watermarks, at-least-once with idempotent upserts, a dead-letter queue and replay from the raw archive.
A warehouse that adds up, for your team?
I'm looking for a team where I can do this work properly. A conversation is easily arranged.