Senior Staff Data Engineer

Overview

As Senior Staff Data Engineer in Computational Innovation’s AI Accelerator, you will own the data engineering architecture and technical direction to build a trustworthy data foundation for foundation-model work. You will define how multimodal biomedical data are integrated and provisioned to support model training, evaluation, and deployment. You will lead high-impact data engineering challenges and shape governance, metadata, and reproducibility standards. This role blends strategic leadership with hands-on execution to accelerate disease understanding and therapeutic discovery.

Responsibilities

• Define the data engineering architecture and roadmap aligned to AI Accelerator priorities
• Design and evolve a layered, medallion-style data architecture and provisioning patterns for multimodal data
• Establish data quality controls, CI/CD for data, reproducibility standards, data contracts, metadata management and dataset versioning
• Lead complex data engineering challenges across modalities including genomics, transcriptomics, imaging, clinical and real-world datasets
• Collaborate with Data Excellence and IT on governance, ontologies, metadata harmonisation, stewardship and shared data foundations
• Mentor and lead data engineers, establish ways of working, onboarding and escalation handling for difficult data challenges

Key requirements

• PhD or MSc in STEM
• Extensive senior-level data engineering experience defining architecture, standards and direction
• Experience with large-scale data engineering, distributed processing, cloud data platforms and data integration
• Strong understanding of biomedical/healthcare data domains (genomics, transcriptomics, multi-omics, imaging, EHR, real-world data)
• Experience implementing data governance in practice (metadata, lineage, provenance, ontologies, cataloguing, FAIR, TREs)
• Strong collaboration and ability to communicate complex technical concepts to diverse stakeholders

• Collaboration and influencing across technical and non-technical stakeholders
• Clear communication of complex concepts
• Mentoring and team leadership
• Large-scale data engineering
• Distributed processing frameworks
• Pipeline orchestration

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