Data Engineer: Cloud-Native Pipelines for Fusion Energy

Sigma Software; España; On-site; EUR 65,000 - 95,000; Full time

Job summary

Sigma Software is seeking a Data Engineer to help build scalable cloud-native data platforms for a fusion energy project. You will work with Databricks, Apache Spark, AWS, and Azure to solve complex data engineering challenges in a research‑intensive environment.

You will collaborate with scientists and engineers to support analytics, ML workflows, and operations while focusing on data quality, governance, and reproducibility.

Qualifications

• 3+ years of commercial Data Engineering experience.
• Hands-on production Databricks experience required.
• Strong Python, PySpark, and SQL skills.
• Deep knowledge of Delta Lake and table optimization.
• Experience with AWS and Azure cloud services.
• Familiarity with CI/CD, IaC and Terraform.

Responsibilities

• Design, develop, and maintain scalable data platforms and pipelines for analytics and operations.
• Build distributed data processing workflows using Apache Spark and Databricks.
• Develop cloud-native data solutions on Azure and AWS.
• Model and optimize data structures for reporting and downstream use.
• Integrate data from multiple sources with quality, lineage, and traceability.
• Implement data quality controls, schema validation, and monitoring.
• Improve performance, scalability, reliability, and cost efficiency of pipelines.
• Collaborate with scientists and engineers to translate requirements into technical solutions.
• Contribute to CI/CD, infrastructure automation, and best practices.
• Explore new technologies to advance the data engineering ecosystem.

Skills

Databricks

Python

PySpark

SQL

Delta Lake

AWS

Azure

CI/CD

Terraform

Machine learning workflows

Tools

Databricks

Unity Catalog

Lakehouse

Terraform

Docker

AWS Services

Job description

Sigma Software is seeking a Data Engineer to help build scalable cloud-native data platforms for a fusion energy project. You will work with Databricks, Apache Spark, AWS, and Azure to solve complex data engineering challenges in a research‑intensive environment.

You will collaborate with scientists and engineers to support analytics, ML workflows, and operations while focusing on data quality, governance, and reproducibility.