Data engineer in cybersecurity

At Hadrian, that's exactly what we're building: the future of offensive cybersecurity, powered by autonomous AI agents that think like hackers. Founded in Amsterdam in 2021, we're on a mission to change the way organisations defend themselves by helping them find and fix security weaknesses before attackers can exploit them.

• Own and operate Hadrian's end-to-end data platform, ensuring reliability, scalability, and performance across the entire data stack.
• Lead strategic data architecture initiatives and help shape the future of Hadrian's AI and MLOps data foundation.
• Maintain, troubleshoot, and optimize data integrations, connectors, and pipelines built on Airbyte, dbt, and BigQuery.
• Build and evolve data models, transformations, and semantic layers that power analytics, business operations, and AI initiatives.
• Drive operational excellence by improving platform reliability, monitoring, and incident response processes.
• Partner with stakeholders across Engineering, AI, Security, and Business teams to deliver trusted and actionable data products.
• Evaluate and implement modern data platform capabilities, including orchestration, streaming architectures, and Lakehouse technologies.
• Build tooling and automation that improves developer and analyst productivity.

• Have 5+ years of professional Data Engineering experience, ideally in a fast-paced startup or scale-up environment.
• Have deep hands-on experience with Airbyte and understand data replication, connector management, and troubleshooting at scale.
• Are highly proficient with dbt, SQL, and Python, and have extensive experience building and maintaining transformation pipelines.
• Have experience working with GCP and AWS environments, including managing data storage (S3 / Cloud Storage) and identity management (IAM).
• Can operate independently and take full ownership of a business-critical data platform without requiring significant day-to-day guidance.
• Apply strong software engineering principles and best practices to data infrastructure.
• Balance operational excellence with long-term architectural thinking.
• Use AI pragmatically to accelerate workflows while maintaining high standards of quality and reliability.
• Communicate effectively with both technical and non-technical stakeholders.
• Share our values and work in accordance with them.
• Будет плюсом: Experience with Kafka, Strimzi, Redis, or other streaming, caching, and in-memory data platforms; Experience utilizing Redis or dedicated feature stores to support real-time MLOps and AI/LLM workloads; Familiarity with Lakehouse architectures and Semantic Layer implementations; Experience with Kubernetes, Helm, Terraform/OpenTofu, or cloud infrastructure automation; Experience with Scaleway or multi-cloud environments; Contributions to open-source data, analytics, or MLOps projects; Experience deploying or optimizing infrastructure supporting LLM workloads and AI applications

• Unlimited paid holiday
• Stock options package
• Mobile phone stipend
• Work-from-home budget
• Business travel card
• Swapfiets
• Referral bonuses