Data Engineer

• A gross monthly salary between €4,600 and €5,800
• Electric lease car or mobility allowance
• MacBook or Windows laptop
• A Claude Code premium seat
• 25 vacation days and 8% holiday allowance per year
• €4,000 training budget per year
• An inspiring workplace in the Titaan (The Hague)
• Fun outings ranging from culinary sensations and social & sporting activities to family day.

Someone has to make the data reliable before anything intelligent can be built upon it. At Lionsville, that person is you, working on systems where a wrong number doesn't just result in an ugly chart, but a wrong decision, gone live.

You own the path from operational systems to platforms that people act upon: in the maritime sector, logistics, industry, and critical infrastructure, and over time for multiple clients and domains. The data is real, imperfect, and arrives at inconvenient moments. Making that data reliable is the work.

You work as an AI maestro
You leave most of the programming work to your AI agent. You ensure that the specifications capture exactly what the client needs, and that the agent has the right context and skills to build it.

From data feed to data contract
You draft the first data contract based on real payloads, not on assumptions. These can be event streams, scheduled extracts, database views, and yes, even spreadsheets. For every field, you define what it means, how current it is, what the source is, and what conclusions the platform is allowed to draw from it. You measure first and model only afterwards. Before anyone starts tinkering with an algorithm, you map out what the data actually shows: volumes per segment and time window, throughput, completeness, and the gaps that nobody was talking about. Your analysis forms the basis upon which the entire plan is built, and you are the one who makes that clear at the table.

You build robust data pipelines
You extract data from systems that were never intended for that purpose, transform it into data models that people understand, and set up the orchestration so that everything runs autonomously. Think of incremental loads, idempotent reprocessing, and backfills. You consciously account for data arriving too late or in the wrong order in your design, so that you don't discover it only once in production.

You work with diverse technologies: batch and streaming, SQL and NoSQL, data warehouses and data lakes, and cloud-agnostic architectures. This might be with the major cloud providers, but it could just as easily be on-premises or with European parties, where systems communicate via APIs and event-driven solutions. Which technology a project uses is a conscious choice per situation, and you contribute your thinking on that.

Data quality is not an afterthought
Stale data must also be recognizable as stale and must never pass for current. If crucial input is missing, the system should refrain from giving advice rather than unnoticedly giving worse advice. You build the controls that ensure this and the monitoring that shows at a glance how every data feed is doing. And you stick to those quality requirements, even as the deadline approaches.