Hire a data engineer or bring one in on secondment

Whatever your team needs: making new data sources accessible, building or fixing pipelines, setting up data models, adding tests and monitoring, or carrying out a migration. The engineer works as part of your team. We have experience working within client teams on projects too: at Waternet, we set up the MLOps workflow as part of its Data Lab team, and we still maintain it.

Our data engineers are employed by Data Science Lab. You have one point of contact for selection, onboarding and follow-up, and we stay involved throughout the assignment. The engineer can draw on colleagues, our internal lab and the Techdays where we share knowledge. With a freelancer, you handle selection and supervision yourself, and you are responsible for the arrangements governing their independent status.

Since 1 January 2025, the Dutch Tax Administration has resumed enforcement against false self-employment. Whether someone is effectively working as an employee is assessed based on how the arrangement works in practice, including the degree of supervision, their integration into the organisation and their independence as a business. If you hire a self-employed contractor, that is a question for you as the client. When you hire through Data Science Lab, our colleagues are employed by us. Have HR, procurement or an adviser review your own hiring policy.

They work with Python and SQL on Azure, Databricks and Microsoft Fabric, among other tools. We often use dbt for transformations and data tests, and define infrastructure as code, for example with Terraform. For Fokker Services Group, we are building a data platform with Databricks, Azure, dbt and SQL that cleans and models data using the medallion architecture. If you use Apache Airflow or another tool, mention it in the first conversation.

An account in your identity management system, such as Microsoft Entra ID; access to the repository and the development and test environments; and permissions for the data needed for the work. Grant permissions through groups and only where necessary, so you can easily revoke them at the end. Also establish who the client contact is, which standards apply and where the backlog is kept.

By planning for knowledge transfer from the start. Code stays in your repository, transformations have tests and descriptions, for example in dbt, and design decisions are recorded where your team can find them. Where appropriate, the engineer works alongside an internal colleague on the same tasks. Knowledge transfer happens when you want it, not just at the end of the assignment.

If the work is ongoing, you may be better off hiring someone directly. If you want a defined outcome rather than additional capacity, a project is a better fit. If the problem is unclear data definitions or ownership, extra engineering will do little to solve it, and it is better to start with data governance. If you only need reports, a BI specialist often makes more sense than a data engineer.