Data Engineer (Google Cloud & Digital Analytics)

Job description

Data Engineer (Google Cloud & Digital Analytics)
Freelance / Interim | Hybrid (Amsterdam) | Start December 2026

Our client is a well-known Dutch consumer brand with a substantial digital and e-commerce operation. Their digital department runs on Google Cloud, and the data platform underneath it drives marketing decisions, customer analytics and a growing set of machine learning use cases. You are joining to take ownership of that platform as the current engineer moves on.

This is a broad remit held by one person. You own the data models, the pipelines and the architecture, and you work directly with marketing and digital stakeholders rather than through layers. You also bring other members of the team up the curve, so part of the job is teaching, not just building.

Responsibilities

Data platform and pipelines

• You own and extend the data architecture on Google Cloud, with BigQuery at its centre.
• You build and maintain transformation models in dbt.
• You design, run and monitor orchestrated pipelines in Airflow.
• You write production Python for ingestion, transformation and automation.

Digital and marketing analytics

• You own the GA4 implementation and its BigQuery export, turning raw event data into models the business can use.
• You manage Google Tag Manager, including tagging strategy, data layer design and governance.
• You work directly with marketing and digital teams to translate their questions into data they can act on.

Machine learning

• You deploy, monitor and maintain machine learning models in production.
• You build the pipelines and infrastructure that keep those models fed and running.

Enablement

• You mentor others in the team, raising the standard of engineering practice around you.
• You document what you build, so the platform does not depend on one person.

Job requirements

• Strong commercial experience as a data engineer on Google Cloud Platform, with deep BigQuery knowledge.
• Hands-on expertise in dbt for data modelling and transformation.
• Production experience with Airflow or an equivalent orchestrator.
• Strong Python for data engineering.
• Practical GA4 experience, including working with the raw BigQuery export.
• Working knowledge of Google Tag Manager and digital analytics implementation.
• MLOps experience: deploying and maintaining models in production, not only building them.
• A track record of mentoring or coaching other engineers.
• Comfortable working directly with non-technical marketing and commercial stakeholders.
• Available from December 2026, with the right to work in the EU.

The following are a plus:

• Experience in retail, e-commerce or consumer brands.
• Exposure to consent management and privacy-compliant tracking.
• Dutch language skills.

Why this role

You own the whole stack rather than a slice of it, from tag to model to production. The client is a consumer brand with real scale, so the data you work with reflects genuine customer behaviour rather than a proof of concept. The remit is wide and the autonomy is real, which suits someone who wants to shape a platform rather than maintain someone else's.