Data Engineer
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Summary
Data engineer building and maintaining production data pipelines with dbt and Python on cloud platforms (GCP preferred, with BigQuery and/or Databricks/PySpark), covering CI/CD, IaC, containerisation, data quality testing and governance. On-site role in the Netherlands, collaborating with data management leads, product owners and engineers.
- Strong data engineering fundamentals, incl but not limited to writing pipelines in dbt and Python and production-grade code on cloud platforms (GCP preferred), CI/CD, IaC, containerisation
- Hands-on experience with BigQuery and Databricks/PySpark (at least one is required, but preference will be given to those with both)
- Ability to review and improve processes by introducing new ways of e.g. coding, reviewing, testing through smart (not sloppy!) use of AI
- Basics in data visualisation (ideally Data Studio, PowerBI would be a big plus)
- Experience with data quality frameworks: defining rules, writing tests, investigating failures
- Knowledge of data structures: data models, databases, data warehousing, and how data flows across systems
- Experience with customer data in general (profile data, events such as purchases and interactions) is a strong plus
- Experience with classifications and unified profile data products is a strong plus
- Knowledge of data governance frameworks, metadata management, and data cataloguing best practices is a plus
- Communication and Interpersonal Skills
- Perseverance and proactive coping with uncertainty: we still have a lot to figure out and need someone excited about that rather than overwhelmed
- Collaboration: strong communication skills to work with Country Data Management Leads, Product Owners, Data Engineers, and Software Engineers
- Constructive problem-solving: ability to assess data issues, identify owners, and drive resolution through stakeholder collaboration
- Ability to translate business requirements into data products, data quality rules, and governance standards
What 3 things from the box above are most important?
- Strong data engineering fundamentals, incl but not limited to writing pipelines in dbt and Python and production-grade code on cloud platforms (GCP preferred), CI/CD, IaC, containerisation
- Hands-on experience with BigQuery and Databricks/PySpark (at least one is required, but preference will be given to those with both)
- Perseverance and proactive coping with uncertainty: we still have a lot to figure out and need someone excited about that rather than overwhelmed.