Data Engineer

Scientific data systems continue to expand rapidly as global research institutions manage increasingly complex biological datasets. This Data Engineer role offers experienced professionals the opportunity to support world leading structural biology databases and bioinformatics platforms.

Furthermore, the position combines advanced data engineering, database migration, and pipeline optimization responsibilities across large scale scientific systems. It also provides exposure to international research collaboration, high performance computing environments, and globally significant biological data resources.

This position focuses on designing, optimizing, and maintaining large scale data pipelines that support structural biology research. The Data Engineer will improve data processing systems to ensure efficiency, scalability, and reliability across multiple scientific databases. Additionally, the role involves working closely with bioinformaticians and developers to integrate complex datasets into production systems.

You will analyse existing pipelines, identify performance gaps, and implement robust engineering solutions. Moreover, the role requires continuous monitoring of data workflows and contribution to system architecture improvements. Ultimately, this position ensures that global biological data resources remain accurate, accessible, and highly performant.

The European Bioinformatics Institute, part of EMBL, is a leading global research institution focused on biological data storage and analysis. The organization manages internationally recognized resources such as the Protein Data Bank and Alpha Fold Protein Structure Database. Moreover, it supports scientific advancement in healthcare, biodiversity, and life sciences through open data platforms.

The institute operates in a highly collaborative and interdisciplinary research environment. Additionally, it brings together experts in bioinformatics, software engineering, and computational biology. Its mission centers on enabling global scientific discovery through accessible and high quality data infrastructure.

• Analyse and improve existing data pipelines to enhance performance and scalability.
• Develop and maintain ETL processes for large scale biological datasets.
• Integrate data pipelines with bioinformatics tools and scientific applications.
• Monitor system performance and resolve data processing and infrastructure issues.
• Design and implement database solutions across relational database systems.
• Support migration projects between Oracle, Postgre
  
  SQL, and MySQL platforms.
• Optimize SQL queries, indexing strategies, and database performance structures.
• Document data pipelines, workflows, and technical processes for knowledge sharing.
• Collaborate with scientific teams to align data systems with research requirements.
• Evaluate and adopt new data engineering tools and technologies.

• Master’s degree in computer science, bioinformatics, or related technical field preferred.
• Strong proficiency in Python programming and advanced SQL development.
• Extensive experience with relational database systems including Postgre
  
  SQL and Oracle.
• Proven experience in ETL development and large scale data processing workflows.
• Experience with database migration projects including Oracle to Postgre
  
  SQL transitions.
• Strong understanding of data modeling, warehousing, and performance optimization.
• Familiarity with cloud data platforms such as Big Query or Amazon Redshift.
• Ability to work collaboratively in multidisciplinary scientific and technical teams.
• Strong communication skills in English for technical documentation and reporting.
• Experience with big data tools or distributed computing frameworks is advantageous.

This Data Engineer role offers a unique opportunity to contribute to globally significant biological data systems. It combines advanced data engineering with impactful scientific research in a collaborative environment. Furthermore, the position provides strong international exposure within a leading research institution. Ultimately, it is ideal for engineers passionate about large scale data systems and scientific innovation.