Why Data Engineers Choose Twentynext Over a Multinational

Quick summary

A specialist data science consultancy offers data engineers a fundamentally different working environment than a multinational: a narrower focus, deeper expertise, more direct client impact, and greater room for methodological growth. Twentynext brings together Data Engineering, Data Science, Business Intelligence, and AI in one integrated approach, with the CRISP-DM methodology forming the backbone of every project.

• According to BDO Deal Advisory, the Dutch data and AI market is expected to grow to more than €10 billion before 2027, making employer choice a strategic decision right now.
• UWV data shows that the ICT sector typically has around 7 vacancies per job seeker, which means data engineers have real options.
• At Twentynext, professionals work on end-to-end projects, from data architecture through to AI implementation, rather than isolated sub-tasks.
• ISO-certified service and management processes make Twentynext especially attractive to professionals who take continuity and quality seriously.
• The Brainport region around Eindhoven offers one of the highest concentrations of tech talent and clients in the Netherlands.

Introduction (Consultancy)

Maybe you’ve just finished a master’s in Data Science, or maybe you already have a few years of experience as a data engineer. Either way, the job market is wide open. Multinationals offer attractive salary packages and brand names that look impressive on your CV. But six months in, you may find yourself maintaining a single legacy pipeline while three layers of management debate whether you’re allowed to test a new tool.

That pattern is familiar to a lot of technical professionals. The real question isn’t just where you’ll earn the most, but where you’ll grow the fastest and do the most meaningful work. Twentynext is a Dutch data and AI consultancy based in Eindhoven, helping organisations become more data-driven through Data Science, Data Engineering, Business Intelligence, and AI implementation. Its way of working is fundamentally different from that of large consulting firms and multinationals. In this article, we’ll show exactly where that difference lies and help data professionals make a more informed choice.

And this matters, because the Dutch market is tight. According to UWV, roles in Data Engineering and related disciplines remain structurally understaffed, with around 7 vacancies for every ICT job seeker. In other words: as a professional, you genuinely have the luxury of choice. It’s worth using that choice wisely.

What makes a specialist data science consultancy different? (Data and reporting environment)

A specialist data science consultancy stands apart from a multinational not so much because of its size, but because of its focus. In a large organisation, you’re often one link in a long chain. At a consultancy like Twentynext, you’re involved in the full journey: from the first business question to the production environment the client uses every day.

Breadth versus depth in projects

At multinationals, data engineers are often assigned to isolated tasks within a predefined scope. Think maintaining existing pipelines for one business unit, with little visibility into the wider architecture. At Twentynext, engineers work on projects that combine Data Engineering, Business Intelligence, and AI, depending on the client’s business challenge.

Take a mid-sized logistics client in the south of the Netherlands. A data engineer discovers that reporting takes hours every week because data is scattered across multiple systems. At Twentynext, that engineer works alongside a BI consultant and an AI specialist to redesign the full architecture, rather than simply patching one pipeline. That kind of end-to-end involvement accelerates professional growth in a big way.

Direct business impact as the daily benchmark

One thing professionals often say after moving from a multinational to a specialist consultancy is this: in a large company, it can take months before you know whether your work actually mattered. At Twentynext, the process runs the other way around. Every project starts with the client’s business challenge, not with a preferred technology stack. That means engineers are thinking early on about questions like: what decision will the client need to make, and what data is needed to support it?

This way of working fits perfectly with the thinking behind CRISP-DM. The framework Twentynext uses consistently begins with a clear analysis of the business objective before a single line of code is written.

Governance and ownership

Another difference experienced engineers often mention is the gap between decision-making and execution. Large organisations tend to have extensive approval processes. At Twentynext, engineers work in smaller teams with direct client contact and short lines to senior colleagues. That means more autonomy, but also more ownership over the quality of the final result.

What to look for yourself:

• Ask in interviews how many projects an engineer typically works on per year. Fewer than two full end-to-end projects annually may be a sign you’re stepping into a maintenance role.
• Check whether the company uses a structured project methodology such as CRISP-DM or something comparable. Without that, repeatable learning becomes much harder.
• Ask whether engineers are involved in the business problem itself or only in the technical execution.
• Find out whether there is an active R&D budget. That often determines whether you’ll learn new techniques or simply maintain existing systems.

How Twentynext drives growth through CRISP-DM and R&D

CRISP-DM (Cross Industry Standard Process for Data Mining) is an iterative six-stage methodology for structuring data projects from business understanding through to deployment. It remains one of the most widely used frameworks in the industry, and at Twentynext it forms the backbone of every data science project.

Why CRISP-DM helps professionals grow faster

Many data engineers who start at larger organisations spend a long time working in just one phase of a project, often modelling or data preparation. CRISP-DM deliberately pushes teams to loop back to earlier phases whenever new insights emerge. That iterative principle means engineers at Twentynext move through all stages of a data science project in a relatively short time, including stakeholder communication and evaluation of business outcomes.

A junior data scientist who joins client business discussions within the first few months is not only developing technical skills, but also learning how to think in context. That’s the difference between someone who builds models and someone who solves problems. Anyone who has read about the AI paradox in Dutch organisations will understand how critical that link between data and business need really is.

Active R&D as a growth engine for engineers

Twentynext invests actively in Research & Development. For data engineers, that’s not just a long-term advantage but an immediate one. Working on current AI challenges in an R&D setting helps professionals build skills that are in strong demand across the market. Roles in Data Engineering, cloud architecture, and related fields remain consistently hard to fill, as shown by UWV figures on ICT labour shortages.

In multinationals, R&D is often centralised in dedicated departments that engineers have to apply to join. At Twentynext, R&D is part of day-to-day practice. That means engineers get hands-on experience sooner with generative AI applications, new data architecture patterns, and the latest tooling, instead of waiting for approval from a central innovation team.

The EU AI Act as a core professional skill

The EU AI Act has been in force since 1 August 2024. From 2 February 2025, organisations using AI are required to work on AI literacy, as confirmed by the Dutch Data Protection Authority. For data engineers, that means governance and responsible AI use are no longer optional skills. Twentynext prepares engineers for this through targeted training and ISO-certified management processes, which adds direct value for professionals guiding clients through AI projects.

What to look for yourself:

• Ask in interviews how many hours per quarter are available for R&D or experimental work. Less than one day a month is often a sign that innovation exists more on paper than in practice.
• Check whether the consultancy offers training in AI governance and the EU AI Act. From February 2025 onward, this is a required capability for organisations using AI.
• Ask whether engineers at Twentynext are involved in all six CRISP-DM phases or only in modelling and data preparation.
• Compare how many different project domains an engineer typically works across over two years.

Detailed comparison: Twentynext vs. a multinational

The table makes the trade-off fairly clear. The main advantages of a multinational tend to be brand recognition and salary level. Twentynext consistently scores better on the factors that determine how quickly an engineer grows and how much influence they have on the final outcome.

Which environment suits you best as a data professional?

Choosing an employer is really a choice about how you want to learn over the next few years, not just what you want to earn right now. For data engineers aiming to move quickly into senior or lead roles, the working environment matters enormously.

Signs that a specialist consultancy is the better fit

Twentynext is likely to suit you if this sounds familiar: you want to understand why a client needs a particular data solution, not just how to build it. You get frustrated when you spend months on something that never gets used. And you want direct client feedback, not updates filtered through four layers of management.

The Netherlands and surrounding countries account for a significant share of European demand for data scientists. According to the CBI, the Netherlands, the United Kingdom, Germany, France, and Poland together account for nearly 72% of all data scientist vacancies in Europe. That makes choosing a Dutch employer strategically attractive, especially in a knowledge-intensive region like Brainport.

Signs that a multinational may be the better fit

A multinational may suit you better if your main goal is to specialise deeply in one technology or platform, and if international mobility and a broad internal network matter more to you than direct business impact. There’s nothing wrong with that. But if you want to grow quickly in a broad data role, a large company will usually make you wait longer for the chance to prove yourself.

A practical decision framework

Ask yourself three questions:

1. In two years’ time, do I want to be broadly capable across Data Engineering, Data Science, and AI, or deeply specialised in one technology?
2. Do I find direct client interaction and business impact motivating, or do I prefer a more abstract technical environment?
3. Do I value methodological guidance from senior colleagues more, or a large internal network?

If your answer to the first two is yes, you’re likely a better match for the way a specialist data science consultancy works. For clients and professionals in North Brabant and the wider Brainport region, Twentynext offers that combination of technical depth and business proximity every day. You can find more about the approach on the Twentynext website.

What to look for yourself:

• Write down the three skills you want to have in two years and assess which employer will help you build them fastest.
• In an interview, ask for a concrete example of a project from the last quarter: how did it start, who made the decisions, and what was the outcome?
• Compare the average time it takes before someone gets independent project responsibility. At specialist consultancies, that’s usually shorter than at large consulting firms.
• Check whether the employer has ISO-certified management processes. That tells you something about the organisation’s professional maturity.

Frequently asked questions

What is a data science consultancy, and how is it different from a multinational?

A data science consultancy is a specialist organisation focused entirely on data-related services such as Data Engineering, Data Science, Business Intelligence, and AI implementation. Unlike multinationals, where data teams are often part of a broader IT or consulting business, a specialist consultancy keeps its full focus on data. That usually means engineers work across a broader scope and gain quicker access to the latest methods and tools, because it’s the core business rather than a side function.

How does Twentynext support data engineers in their professional development?

Twentynext supports data engineers through the structured CRISP-DM methodology, where every project moves through all stages from business understanding to deployment. As a result, engineers don’t just become stronger technically; they also learn how to handle client conversations and translate business challenges into data solutions. On top of that, Twentynext offers active R&D opportunities and training aligned with current requirements such as the EU AI Act, helping engineers build the skills the market is asking for most. You can read more about the approach at twentynext.nl.

Why is the Dutch data and AI market attractive for data professionals?

The Dutch market is known across Europe for strong demand for data talent. According to BDO Deal Advisory, the Dutch data and AI market is expected to grow beyond €10 billion before 2027, with data and AI services projected to rise by around 59%. Combined with the structural shortage in the ICT labour market, where there are typically around 7 vacancies per ICT job seeker, data engineers in the Netherlands enjoy an exceptionally strong negotiating position and a wide range of opportunities.

What are ISO-certified management processes, and why do they matter for data engineers?

ISO-certified management processes are standardised service and operational procedures that have been tested and certified by an external party. For data engineers, they matter because they signal a working environment with clear quality standards and continuity requirements, reflecting the consultancy’s professional maturity. At Twentynext, this means business-critical data environments are managed according to documented processes, giving both clients and engineers confidence in how work is delivered and what quality standards to expect.

What does a typical CRISP-DM project look like at a specialist consultancy?

CRISP-DM consists of six phases: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation, and Deployment. At Twentynext, every project moves through these phases iteratively, with engineers and clients working together to make sure the final result stays aligned with the original business question. That may sound theoretical, but in practice it means an engineer is already discussing with the client in week one what a useful outcome looks like, and technical decisions are shaped around that from the start. That makes it far less likely that a model ends up being built and never used.

Conclusion

The market for data engineers and data scientists in the Netherlands remains structurally tight. That shortage gives professionals room to choose where they will truly grow, not just where they can earn the highest salary. The key difference between a multinational and a specialist data science consultancy is not salary, but project ownership, methodological support, and the speed at which you develop.

Twentynext combines a structured CRISP-DM approach with active R&D, ISO-certified management processes, and a focus on the full data and AI value chain. That makes it an attractive choice for data professionals who want to grow quickly into senior-level roles and see direct impact on client business results. For projects in Eindhoven, Tilburg, Den Bosch, or elsewhere in North Brabant and the Netherlands, Twentynext offers that mix of technical depth and close connection to the business.

If you’re seriously considering the choice, you can learn more about Twentynext’s way of working and services at twentynext.nl.