What is data engineering? Definition, tasks and why SMEs need it

Key Takeaways: Data engineering is the discipline that turns raw data from separate systems into reliable, reusable data pipelines. It is the foundation under every dashboard, every AI model and every data-driven decision.

Data engineering is the field concerned with collecting, cleaning, connecting and preparing data from different sources, so that it is reliably and automatically available for analysis and reporting. Where data science is about insights and predictions, data engineering is about the infrastructure underneath: the pipelines, storage and connections that ensure data is correct and on time. For SMEs, data engineering is often the difference between manual Excel work and a scalable, automated data foundation the whole organisation can build on.

What does a data engineer do?

A data engineer builds and maintains the systems that move data from A to B. In concrete terms that means: pulling data from sources such as your CRM, accounting package, webshop or ERP, cleaning and standardising that data, and then writing it to a central place where analysts and dashboards can access it.

The heart of this work is the data pipeline: an automated process that processes data at fixed moments without anyone having to intervene manually. A data engineer makes sure those pipelines are robust, handle errors and keep working when sources change.

In addition, a data engineer guards data quality and architecture. He or she decides how data is modelled, where it is stored and how systems are connected securely. The result is a reliable data foundation that the rest of the organisation can trust blindly.

Data engineering vs. data science

The short answer: data engineering builds the pipeline, data science uses what comes out of it. A data engineer ensures data is reliable, clean and available; a data scientist uses that data to find patterns, train models and make predictions.

The two roles are complementary and sequential. Without solid engineering, a data scientist works with incomplete or inconsistent data, and the outcomes cannot be trusted. In practice, a good sixty to eighty percent of a data project goes into collecting and preparing data, not into the modelling itself.

For those who want to separate the roles more sharply, including the data analyst in between, we have a separate article on the difference between data analyst, engineer and scientist. For most SMEs the rule is: start with engineering, because without a foundation the rest makes little sense.

Why does an SME need data engineering?

Because most SMEs have their data spread across separate systems that do not talk to each other. The accounting lives in package A, the customer data in package B, the sales figures in a webshop, and the overview only emerges when someone manually pastes everything into Excel. That is error-prone, time-consuming and not scalable.

Data engineering solves this by connecting those sources automatically and preparing the data centrally and consistently. The result is that reports are always up to date, everyone sees the same figures, and employees have time left for analysis instead of collecting data.

Moreover, data engineering is the precondition for everything that comes after. If you want to deploy dashboards, predictions or AI, you first need reliable, structured data. Without a foundation you build on sand, no matter how good your analyst or model is.

When do you need a data engineer?

As soon as manually merging data structurally costs time or produces errors. A good rule of thumb: if someone on your team spends several hours every week pulling data from systems and copying it into Excel, then a data pipeline quickly pays for itself.

Other clear signals are reports that do not line up, a new system that needs to be connected, or plans for a dashboard or AI application that currently get stuck on messy data. Growth makes it more urgent too: what works for ten customers in Excel breaks at a hundred.

You do not have to hire someone on a permanent contract right away. Many SMEs start with a data engineer on a project basis or through a consultancy, who lays down the foundation and leaves things maintainable. Whether that is cheaper than permanent staff is something we explain in our article on the cost of hiring a data engineer.

What does data engineering cost?

A first, well-defined project, such as connecting a few systems or building a data pipeline to a dashboard, typically costs an SME between 5,000 and 25,000 euros. The price depends mainly on the number of sources, the state of the data and the complexity of the connections.

Hiring a data engineer on an hourly basis usually costs between 90 and 150 euros per hour, depending on experience and whether you work via a freelancer or an agency. For an ongoing ETL pipeline, limited maintenance and hosting costs are added, often a few tens to a few hundred euros per month.

An important advantage for Dutch companies: developing data pipelines and connections often falls under the WBSO scheme, which provides a tax compensation for part of the labour costs of the development. This significantly lowers the net investment.

Stratalytic and data engineering

• We build reliable data pipelines that automatically connect your separate systems, so you never have to merge data manually again.
• We work pragmatically and at SME scale: no over-dimensioned platforms, but a foundation that fits your size and budget.
• We deliver maintainable work and transfer knowledge, so you do not stay dependent on an external party.
• We help you make use of the WBSO subsidy on the development hours, so your net investment turns out lower.
• Whether you need a first connection or a complete data foundation: we start small and grow along with you.

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Frequently asked questions

What is data engineering in short?

Data engineering is the discipline that collects, cleans, connects and prepares raw data from separate sources into reliable data pipelines for analysis and reporting. A data engineer builds the infrastructure and pipelines so that data is automatically, consistently and on time available for dashboards, AI models and decision-making within the organisation.

What is the difference between data engineering and data science?

Data engineering builds the pipelines and infrastructure that deliver data reliably; data science then uses that data to train models, make predictions and generate insights. Engineering delivers the source, science delivers the insight. Without solid engineering, data scientists work with unreliable data and their output cannot be trusted.

Does an SME need data engineering?

Yes, as soon as an SME wants to combine data from multiple systems or automate reporting. Many companies collect data manually in Excel, which is error-prone and time-consuming. Data engineering automates that process and provides a reliable data foundation, even with limited teams and budgets.

What does data engineering cost for an SME?

A first data pipeline or integration project for an SME typically costs between 5,000 and 25,000 euros, depending on the number of sources and the complexity. Hiring a data engineer costs roughly 90 to 150 euros per hour. Through WBSO, part of the development costs is often eligible for subsidy, which lowers the net investment.