Data Engineer

Location: The Hague

Darktrace is a global leader in AI for cybersecurity that keeps organizations ahead of the changing threat landscape every day. Founded in 2013, Darktrace provides the essential cybersecurity platform protecting nearly 10,000 organizations from unknown threats using its proprietary AI. The Darktrace Active AI Security Platform TM delivers a proactive approach to cyber resilience to secure the business across the entire digital estate – from network to cloud to email.

Breakthrough innovations from our R&D teams have resulted in over 200 patent applications filed. Darktrace’s platform and services are supported by over 2,400 employees around the world. To learn more, visit

Job Description:

The Data Engineers at Darktrace help design and develop cloud-native data infrastructure that powers AI/ML models within Darktrace produts. They build scalable systems to collect, store, and process data, handling datasets with billions of rows, and supporting the full ML model lifecycle. The position is part of the R&D team in The Hague, and you will be expected to work a minimum of 2 days a week in office.
What will I be doing:  Your work will lay the foundation for future product innovation and support the rollout of model-driven features in the Darktrace products by ensuring reliable data flows from source to model to production. You will help build a data backbone that is easily maintainable and extensible while upholding high standards for data quality, scalability, and cost efficiency.

You will work closely with Data Scientists, MLOps Engineers, and Software Engineers to ensure seamless integration between data infrastructure, ML workflows and the data backend. You’ll contribute to architectural discussions and help implement robust, maintainable solutions aligned with data engineering best practices.
Additionally, you will be responsible for:
Contributing to the design, implementation, and maintenance of data pipelines that power AI/ML models within the Darktrace products
Helping build and maintain cloud-native data platforms that integrate data from various internal and external sources
Designing systems with scalability in mind to support growing data volumes and evolving ML workloads
Optimizing data pipelines for reliability, scalability, and cost efficiency
Assisting in setting up and maintaining CI/CD pipelines for data and ML workloads, with guidance from MLOps and Dev Ops teams
Collaborating closely with Data Scientists, MLOps Engineers, Software Engineers, and Product Owners to understand data needs and deliver solutions
Participating in knowledge sharing and contribute to continuous improvement by applying data engineering best practices
What experience do I need:  To succeed in this role, you’ll need a strong foundation in data engineering and cloud technologies, along with fluency in English and proficiency in Python. You should be able to demonstrate:
Hands‑on experience with data pipelines (ETL/ELT) and workflow orchestration tools such as Apache Airflow
Solid knowledge of SQL/No

SQL databases, data modeling, and schema design
Familiarity with streaming technologies (e.g., Kafka), containerization (Docker, Kubernetes), and at least one major cloud platform – preferably Google Cloud
Exposure to big data frameworks (Spark, Beam)
Infrastructure‑as‑code tools (Terraform)
MLOps practices is a plus
Beyond technical expertise, the role requires strong analytical and critical thinking skills, effective project management, and clear communication of technical findings. You should be results-oriented, collaborative, and adaptable, with a proactive approach to knowledge sharing and documentation. Curiosity and a willingness to learn new technologies will help you thrive in this dynamic environment.
Benefits:
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