Data Engineering Services

Choosing a data engineering company you can hold to a result

Most teams do not need another set of contractors who disappear into a backlog. They need a data engineering company that scopes the work, names the outcome, and is accountable for delivering it. That is the difference between buying capacity and buying a result. When you hire Crux Digits for data engineering services, you are not renting seats by the week — you are commissioning a defined piece of work with a fixed price, a fixed end state, and senior people who stay on it until it ships.

We are a boutique firm in Nieuwegein, in the province of Utrecht, founded in 2022 and serving clients across the Netherlands and Europe. We have 13 delivered case studies behind us, and the founder and MD, Tom Joseph, is involved in the work — not just the sales call. That matters, because the hard parts of data engineering are rarely the tools. They are the judgement calls: what to model, what to throw away, which source of truth wins when two systems disagree. Those calls are made better by senior people who own the result.

Data engineering consulting that ends in a working system

There is a version of data engineering consulting that produces a slide deck and a target architecture, then hands you an invoice and a roadmap you cannot build. We do the opposite. Our consulting is the front door to delivery, not a substitute for it. We start by understanding the decisions your data is supposed to support — the report a CFO actually reads, the model that needs clean features, the operations dashboard a planner checks every morning — and we work backwards from there.

The engagement follows the same fixed ladder as the rest of our work, with prices excluding VAT. An AI Audit and Strategy at 2,500 euro maps your current data estate and the path to a usable foundation. A Proof of Concept at 20,000 euro proves the hardest part works on your real data before anyone commits to scale. A production launch starts from 50,000 euro and delivers the live system. Work that sits outside that ladder runs at roughly 150 euro per hour. You can see the full structure on our pricing page — no hidden day-rate creep, no open-ended retainer.

A data engineering partner, not a staffing agency

This is worth being blunt about, because the market is crowded with the other model. We are a data engineering partner, and we deliver fixed-scope projects. We are not a staffing or body-shop firm. We do not sell dedicated teams, staff augmentation, nearshore pods, or outsourcing arrangements. We will not drop three junior engineers into your Slack and bill until the budget runs out.

What you get instead is a small, senior group that takes responsibility for an outcome and hands you something you own. When the project ends, the solution is yours — the code, the models, the documentation, the deployment. There is no lock-in to a managed team you have to keep paying to keep the lights on. For a lot of buyers, especially founders and finance leaders who have been burned by an open-ended contract, that single distinction is the reason they pick us over a larger consultancy.

• Fixed scope and fixed price — you know the end state and the cost before we start.
• Senior people on the work — the people who scope it are the people who build it.
• You own the result — full handover, no dependency on a rented team.
• EU AI Act and GDPR first — compliance designed in, not bolted on at the end.

Enterprise data engineering, sized to your reality

Enterprise data engineering usually means more sources, stricter governance, and more people who need to trust the same numbers. We bring the rigour that environment demands — clear data contracts, documented lineage, access controls, and a foundation that an audit can stand on — without the overhead of a hundred-person programme. A boutique firm can move faster precisely because there are fewer hand-offs between the person who understands your business and the person writing the transformation.

For larger organisations, the most valuable thing we deliver early is honesty about scope. Not every problem needs a full re-platforming. Sometimes the right first project is a single, reliable feed that ends a recurring monthly argument about whose spreadsheet is correct. We will tell you when a smaller, sharper engagement gets you further than a grand programme — and then we will deliver it.

Data engineering for SaaS, startups and scaleups

Data engineering for SaaS companies has its own shape. Product usage events, billing data, CRM records and support tickets all need to line up before anyone can answer a question as basic as "which accounts are about to churn." For a startup, the priority is a foundation that does not have to be rebuilt at the next funding stage. For a scaleup, it is usually untangling the systems that were stitched together during the rush to growth.

We have done both. The common thread is that founders do not want a year-long platform project — they want a specific, defensible result they can build the next thing on. A fixed-scope Proof of Concept is ideal here: it puts a working data layer in front of your real numbers in weeks, so you can decide on production with evidence rather than a pitch. When that foundation is in place, it is also what makes downstream machine learning and trustworthy reporting genuinely possible, rather than something that breaks the first time the source data shifts. It is the same foundation that turns the analytics and modelling work on top of it from a monthly firefight into something your team can self-serve, because the numbers stop being a matter of opinion.

For investor-facing scaleups there is a second benefit worth naming. Clean, well-governed data is what lets you answer a due-diligence question in an afternoon instead of a fortnight. Cohort retention, gross margin by segment, pipeline conversion — these are only as fast and as credible as the data layer underneath them. We have repeatedly seen a tidy foundation pay for itself the first time a board or an acquirer asks a question that used to take three people and a week of spreadsheet archaeology to answer.

Industry data engineering

The principles are the same across sectors; the data is not. We shape each engagement around how the industry actually runs.

Logistics and supply chain

Telematics, warehouse systems, transport management software and carrier feeds rarely speak the same language. Getting them into one reliable model is what makes route optimisation, ETA prediction and stock visibility real instead of aspirational. This is a sector we know well — see our work on AI and data for logistics for how the foundation feeds the use cases on top.

Manufacturing and industrial

Sensor and machine data, ERP records and quality logs need to be unified before predictive maintenance or yield analysis can earn its keep. The engineering challenge is usually volume and time-series handling, plus reconciling the shop floor with the back office.

Finance and banking

Here governance is not optional. Lineage, auditability and tight access control sit at the centre of the design, and reconciliation across systems has to be exact. We build foundations that a risk or compliance team can defend, which is also what makes safe analytics and modelling possible afterwards.

Data engineering in the Netherlands and Europe

Data engineering Europe means GDPR is a starting constraint, not an afterthought, and the EU AI Act increasingly shapes how data feeding any model must be handled. We design for both from the first diagram: where data lives, who can reach it, and how its journey is documented. Being a Netherlands-based data engineering company serving European clients, we work bilingually in English and Dutch, in your timezone, with people you can actually reach.

A clean, well-governed data foundation is what every other investment quietly depends on. Good analytics, reliable dashboards and dependable models all sit on top of it — and when the foundation is wrong, no amount of clever modelling rescues the result. That is why we treat data engineering as the groundwork for the rest of your AI strategy, not a box to tick on the way to something more exciting.

If you are weighing up a data engineering partner, the most useful next step is a short, honest conversation about what you actually need — which is often less, and sharper, than the brief you arrived with. We will scope it, price it, and tell you plainly whether a 2,500 euro audit, a 20,000 euro proof of concept, or a full production launch is the right place to start. Whichever it is, you will leave with a result you own and a team that stood behind it.