Why Analytics Engineers Are Becoming One of the Most In-Demand Hires in Data in the Netherlands

Over the last 12 months, one trend has become increasingly clear across the Dutch data market: demand for analytics engineers is growing rapidly.

We’re seeing this title come up in more hiring conversations than ever before, particularly as organisations move beyond simply collecting data and focus instead on making it usable, trusted, and accessible across the business.

And this is supported by the data.

According to LinkedIn Talent Insights, there are currently just 634 analytics engineers in the Netherlands, making it a relatively small and specialist talent pool. Only 116 are actively open to work, which helps explain why companies often find hiring in this area challenging.

The shift from data infrastructure to data trust

For years, organisations focused on building platforms, pipelines, and cloud infrastructure. This generated a lot of data, but we weren’t really asking whether we could actually trust that data.

But today, reliable reporting, consistent metrics, and well-defined business logic have become just as moving data from A to B – and that’s where analytics engineering comes in.

Sitting between data engineering and analytics, analytics engineers help transform raw data into well-structured, documented, and tested datasets that the wider business can confidently use.

Why dbt is driving demand 

One of the biggest indicators of this shift is the growth of dbt.

In our 2026 Netherlands market analysis, dbt was the fastest-growing technology among data engineers, increasing by 82% year-on-year, making it the fastest-growing skill in the data engineering talent pool.

That growth reflects a broader trend towards: 

• Building scalable data models 
• Implementing testing and data quality controls
• Standardising transformation workflows
• Creating trusted datasets for business users
• Improving collaboration between engineers and analysts 

For many organisations, dbt is becoming a central component of the modern data stack.

Why companies are creating dedicated analytics engineering roles 

We’re also seeing increased specialisation across data teams. 

Rather than expecting data engineers to own everything from infrastructure through to business-facing models, many organisations are creating dedicated analytics engineering and data platform engineering functions.  

A recent global survey found that only 11% of data professionals felt their data modelling was going well, while 51% said nobody clearly owned the process.  

As data estates become more complex, businesses are recognising that someone needs to take ownership of data modelling, testing and business logic. Increasingly, that responsibility belongs to analytics engineers. 

What hiring managers are looking for 

The strongest analytics engineers typically combine:

• Strong SQL skills
• Hands-on dbt experience
• Data modelling expertise
• Understanding of modern cloud platforms
• Experience working across engineering and analytics teams
• Commercial awareness and stakeholder engagement skills 

Companies are particularly interested in candidates who can bridge technical implementation and business decision-making.

Looking ahead 

A successful data team isn’t just one that collects the most data, but one that creates trust in that data. 

The rapid growth of dbt, the emergence of dedicated analytics engineering teams, and the increasing focus on data quality all point in the same direction: analytics engineering is becoming a core function within modern data organisations. 

For hiring managers, that’s a welcome development, as competition for this specialist talent pool is only likely to increase. 

And for data professionals looking for their next role, it’s one of the most interesting areas of growth in the market today. 

Frequently asked questions 

What does an analytics engineer do?

An analytics engineer sits between data engineering and analytics, transforming raw data into structured, documented and tested datasets that business teams can use. The role focuses on data modelling, quality controls, and business logic. 

Why are analytics engineers in demand in the Netherlands?

Organisations are moving beyond collecting data to making it usable, trusted and accessible. As teams specialise, they are creating dedicated analytics engineering roles to take ownership of business-facing data models, testing and consistent metrics.

Why is dbt important for analytics engineering? 

dbt supports the shift towards scalable data models, testing, and standardised transformation workflows. These practices help teams create trusted datasets and improve collaboration between engineers and analysts.

What skills are hiring managers looking for? 

The article highlights strong SQL skills, hands-on dbt experience, data modelling expertise, and an understanding of modern cloud platforms. Cross-team collaboration, commercial awareness, and stakeholder engagement are also important for connecting technical work with business decisions. 

How does analytics engineering differ from data engineering? 

Data engineering focuses on platforms, pipelines and infrastructure, while analytics engineering focuses on transforming data into reliable business-facing models. Dedicated roles allow teams to give data modelling, testing, and business logic clearer ownership.