Data Engineer

Role overview Build and evolve cloud-based data platforms that support scientific research, analytics, and operational work in the fusion energy field. You’ll develop distributed data processing solutions and work closely with scientists and engineers to support experimentation and machine learning workflows, with a focus on quality, governance, reproducibility, and reliability.

Responsibilities - Design and operate scalable data pipelines and platform components using Databricks and distributed processing tools. - Develop data transformations with Python, PySpark, and SQL, applying Delta Lake patterns and optimization. - Support data governance, lineage, quality, and reproducibility across research and operational workflows. - Build event-driven and asynchronous pipelines and integrate cloud services as needed. - Improve job performance and platform operations, including workflow orchestration. - Collaborate with scientific and engineering partners to serve analytics and machine learning needs.

Requirements - At least 3 years of commercial data engineering experience, including 2 or more years using Databricks in production. - Strong expertise in Delta Lake, including medallion architecture and table optimization. - Advanced Python, PySpark, and SQL skills; practical knowledge of Unity Catalog and Databricks Jobs or Workflows. - Hands-on AWS experience with S3, IAM, and KMS, plus at least one of AWS Batch, ECR, EventBridge, Lambda, or API Gateway. - Experience designing event-driven, asynchronous data pipelines. - Strong knowledge of infrastructure as code and CI/CD.

Benefits and work setup The role is remote in Europe, with Poland and Romania listed as locations. The source also mentions flexible scheduling, continued learning and growth, and medical and legal support.