Senior Data Engineer

Boehringer Ingelheim GmbH; Greater London; On-site; GBP 90,000 - 120,000; Full time

Job summary

Boehringer Ingelheim in London is hiring two Senior Data Engineers for the AI Enablement team. You will design and deliver robust data pipelines transforming harmonised biomedical datasets into AI-ready assets across multi-omics, imaging and health records.

You will own significant workstreams under guidance from the Senior Staff Data Engineer. The role requires a PhD in a quantitative field, hands-on ML data engineering experience, and familiarity with biomedical data governance.

Qualifications

• PhD in Machine Learning, Computer Science, Bioinformatics, Computational Biology or related field.
• Strong hands-on experience in data engineering for machine learning.
• Experience with medical imaging modalities or multi-omics datasets for cross-modal integration.
• Experience with entity linking/record linkage in biomedical/clinical contexts.
• Familiarity with data governance and controlled-access biomedical data environments.

Responsibilities

• Build and maintain data pipelines that enable model training, fine-tuning and inference in production.
• Develop entity linking and cross-modal integration pipelines across imaging, omics and clinical data.
• Ensure data access permissions, consent conditions and usage restrictions are respected in all pipelines.
• Create well-documented, tested code and participate in code reviews within the team.
• Stay current with advances in data engineering tools relevant to biomedical AI.
• Collaborate with AI Systems and IT to ensure infrastructure and tooling support.

Skills

Data engineering

ML pipelines

Entity linking

Cross-modal data

Education

PhD in ML or related field

Tools

Trusted Research Environments

Job description

Most diseases are still poorly understood at a biological level. Despite decades of research, the causal mechanisms driving many conditions remain unclear, limiting our ability to identify the right targets, design the right interventions and bring the right medicines to patients.

The AI Accelerator exists to change that. Based in London and sitting within Computational Innovation (@computationalinnovation), a global organisation spanning computational biology, human genetics, data excellence and AI, the Accelerator’s mission is to build production-quality AI capabilities that deepen our understanding of disease biology and increase probability of success.

We do this by applying neural-based methods across the biomedical data landscape to integrate heterogeneous, multimodal data sources, infer biological relationships and embed causal thinking into what we build. The goal is not just to predict but to explain and understand why disease occurs.

It could be electronic health records and medical imaging to support patient segmentation. It could be ‘omics data to identify novel therapeutic targets. It could be predicting transcriptional change for a given disease-causing variant. It could be simulating the effect of modulating a target of interest.

A core component of the AI Accelerator is AI Enablement, a team that provides the support framework to make our ambitions a technical reality. It could be provisioning integrated, multimodal biomedical data for model training and inference. It could be managing the lifecycle of models provided by AI Systems. It could be working with IT to ensure the right infrastructure and tooling are in place. AI Enablement ensures that the model builders can focus on the technology and that Computational Innovation’s downstream users can leverage accelerator capabilities for real portfolio impact.

THE POSITION

We are seeking two Senior Data Engineers, one with a medical imaging focus and one with a multi-omics focus, to join the AI Enablement team and contribute to the design and delivery of robust data engineering pipelines that transform harmonised biomedical datasets into AI-ready, integrated assets across multi-omics, clinical and health records, and medical imaging data.

You will be an experienced, independent data engineer within AI Enablement, owning significant data engineering workstreams within the broader technical direction and architecture set by the Senior Staff Data Engineer. The pipelines and integrated datasets you build will enable model training, fine-tuning and inference in a production setting.

Key Responsibilities

• Build and maintain entity linking pipelines that connect patients, samples and other biomedical entities across modalities such as imaging, clinical and multi-omics records
• Build and maintain cross-modal integration pipelines that combine linked imaging, mulit-omics and clinical records, each with different formats, scales and structures, into unified assets ready for multimodal model training, fine-tuning and inference
• Ensure pipelines and datasets are built and operated in accordance with data access permissions, consent conditions and usage restrictions, including within Trusted Research Environments or other controlled access settings
• Build and maintain biomedical benchmark datasets with versioning and documentation
• Write clean, well-tested, well-documented code that meets the required engineering standards and contribute to code reviews within the data engineering team
• Stay current with advances in data engineering tooling and practices relevant to biomedical AI

Required Qualifications

• PhD in Machine Learning, Computer Science, Bioinformatics, Computational Biology or a related quantitative field
• Strong hands-on experience in data engineering for machine learning with proficiency in modern data engineering tech stacks
• Experience working with medical imaging modalities (radiology and/or histopathology) or multi-omics modalities (transcriptomics, proteomics) and a working knowledge of other biomedical data modalities sufficient to support cross-modal integration
• Practical experience with entity linking or record linkage, ideally in a biomedical or clinical context, and a strong understanding of biomedical data characteristics such as variant data formats, expression matrices and clinical coding standards such as SNOMED and ICD-10
• Familiarity with data governance frameworks applicable to biomedical and clinical data and Trusted Research Environments or controlled access biomedical data environments

This is a hybridrole withapproximately3 days a week in the office

WHY THIS IS A GREAT PLACE TO WORK

Boehringer Ingelheim has been recognised as aTop Employer in the UK, demonstrating our commitment to building an exceptional workplace through strong people practices and supportive HR policies.

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