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# Senior Data Engineer, Public Data Packages

- **Company:** Ontoforce
- **Location:** Gent, Belgium
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Encodings, Data Validation, Information Engineering, Graph Database, Python (Programming Language), Operational Databases, Search Technologies, Software Engineering, SPARQL, SQL Databases, Technical Data Management Systems, Scripting, Large Language Models, Kubernetes, Data Management, Data Pipelines
- **Published:** August 23, 2026
- **Apply:** https://www.careerjet.be/jobad/be4221006bfdb0ca788206398d46a0c62b

## About the Role

Seven or more years building and operating production data pipelines, including time as the accountable owner of a data domain rather than a contributor to someone else's, with a proven track record of improving performance and maintainability.
Hands-on experience with the modern technical data stack. The specific tools matter less than the perspective: orchestration (Dagster, Airflow, Prefect, or Temporal), transformation and testing tooling, and lineage and cataloguing. We deliberately do not run most of this in house, which is precisely why we want someone who has.
Strong software engineering habits, not just scripting. You structure code that others can maintain, you test it, and you understand what makes performant code performant. We write a lot of Python, so comfort with it matters, though we care more about how you build than which language you used before. Much of our transformation logic runs on our own provenance-tracking engine rather than in SQL, so SQL fluency is welcome but not essential.
Demonstrated experience implementing automated data quality checks and testing frameworks.
Comfort working with Kubernetes. Our public data pipelines run entirely on EKS, so editing deployments, reading logs, and reasoning about Jobs and resource limits will be part of your routine. You do not need to know how to stand up a cluster, but you should be at home operating inside one.
The instinct to ask "how would I know if this were wrong?" and a practice you introduced as a result that outlasted your involvement, whether in testing, review, monitoring, or documentation. To us, "senior" means the improvements you make remain in place after you move on.
An architecture or build-versus-buy decision you owned, and enough time living with the consequences to judge honestly whether you got it right.
Clear communication with non-engineers, the confidence to disagree with us when you have evidence, and openness to customer input.
A deeply technical, analytical enthusiasm for the insights data can deliver.
A process-oriented mindset, with the understanding that scaling depends on continuously improving how the work is done.
Genuine curiosity about the wider data engineering landscape. You keep an eye on emerging tools, frameworks, and approaches, and you are eager to try them, not for their own sake, but to see whether they genuinely improve how you work. You bring a critical, impact-driven mindset to new technology.
The following would strengthen an application, though none are required:
Some exposure to applying ML or LLMs to matching, classification, or entity-resolution problems. Curiosity and familiarity are enough; we are not looking for a research background.
Life sciences or biomedical data experience.
Knowledge graphs in either tradition: RDF and SPARQL, or labelled property graphs.
Experience with ontologies and controlled vocabularies.
Semantic layer or master data management work in any domain.
Data contracts, or vector search and embedding-based retrieval.
We have yet to meet someone strong across all of this, and we do not expect to. Tell us which parts you are strong in and which you would like to grow into.

## Description

We use AI coding assistants daily and expect you to as well. What interests us is not enthusiasm but judgement: knowing where they accelerate the work, where they produce confident but incorrect output, and how you tell the difference.


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