Architect, deploy, and operate scalable Apache Spark environments on Nebul’s sovereign AI cloud.
Design and optimize Spark workloads for GPU-accelerated and distributed performance.
Define and implement best practices for security, monitoring, governance, and data protection.
Partner closely with product, engineering, and customer teams to shape our managed Spark offering.
Evaluate and integrate complementary technologies (e.g., Delta Lake, Lakehouse components, tooling).
Support early customer pilots and translate feedback into roadmap improvements.
Develop automation and CI/CD deployment models to ensure reliability, repeatability, and efficiency.
Document architectures, operational procedures, and performance benchmarks.

4–7 years of experience working with Apache Spark in production environments.
Strong deep-dive knowledge of Spark internals: performance tuning, partition strategies, caching, and shuffle management.
Hands-on deployment experience in Kubernetes, cloud infrastructure, or on-prem clusters.
Solid understanding of distributed data platforms (e.g., Databricks, EMR, Hadoop, Lakehouse architectures).
Strong scripting and automation skills (Python / Scala preferred).
Ability to translate client needs into technical architectures and operational models.
Familiarity with cloud-security principles and infrastructure-as-code practices.
Valid EU work permit (no sponsorship currently available).
Fluent in English (Dutch not required).

Big Data Engineer, Spark

Key skills