Scientific Data Platform Engineer

Job summary

The Data Engineer will establish a shared data foundation for a complex scientific environment, enhancing the connectivity and traceability of scientific data. Key responsibilities include designing scalable data architecture, creating reusable data foundations for bioinformatics and AI, and collaborating with scientists and IT teams to translate complex questions into practical data solutions.

Qualifications

• Around five years' experience in data engineering, software engineering or a related data-platform role, with experience taking ownership of technical design
• Strong hands-on experience with Python and SQL
• Experience designing data models and data architectures across multiple systems and building and operating production-quality ETL/ELT pipelines and automated data workflows
• Experience integrating heterogeneous data sources, databases and APIs
• Experience with workflow orchestration tooling (e.g. Airflow, Dagster or dbt) for scheduling and operating production data pipelines
• Good understanding of data quality, metadata, lineage, testing and reproducibility
• Ability to evaluate technical options and make pragmatic, scalable and cost-aware architecture decisions
• Strong problem-solving skills and the ability to translate ambiguous requirements into implementable solutions
• Strong communication and collaboration skills across technical and non-technical teams

Responsibilities

• Design and build scalable data architecture, models, pipelines and integrations across scientific and business systems
• Establish consistent approaches to identifiers, metadata, data quality, lineage and traceability
• Create reusable data foundations that support bioinformatics, analytics, machine learning and AI
• Evaluate technical options and help establish data-engineering standards and best practices
• Work closely with scientists, Bioinformatics and IT to translate complex questions into practical data solutions

Skills

Relocation