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 DevOps 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/NoSQL 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), and 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.