Research Data Engineer

About Relation

Relation is a sector defining TechBio company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics from patient tissue, functional assays, and machine learning to drive disease understanding, from cause to cure.

About Relation

Relation is a sector defining TechBio company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics from patient tissue, functional assays, and machine learning to drive disease understanding, from cause to cure. We are scaling rapidly and building a team of exceptional individuals to push the boundaries of drug discovery. You will work in highly interdisciplinary teams where biology, computation, and engineering come together to solve complex problems that have not been solved before. Our state-of-the-art wet and dry labs in the heart of London are designed to accelerate this integration and translate insight into impact. We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the basis of gender, sexual orientation, marital or civil partnership status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age. By joining Relation, you will help define how medicines are discovered and deliver meaningful impact for patients.

The opportunity

Relation is offering an outstanding opportunity for a Research Data Engineer to design and build the data systems that power the next generation of predictive models of cellular behaviour. We generate complex and high-dimensional datasets across modalities at scale. What we can model, and the pace at which we iterate, is determined by the quality of the data layer. This role focuses on making our datasets efficient for analysis and ML model training through deliberate systems design: storage layouts, access patterns, distributed systems to move data from instrument to models, modality-specific format decisions, query and serving layers tuned for GPU-saturated training, and upholding the data contract between the wet lab and ML teams.

Day to day, you will

• Design, build, and maintain scalable data pipelines that ingest multi-modal scientific data.
• Optimise data movement, storage layouts, and access patterns for analytical and ML workloads.
• Stand up and evolve cloud-native data lake / lakehouse infrastructure.
• Implement data versioning, lineage, and quality monitoring.
• Partner with data scientists day-to-day to ensure fast and seamless data workflows.
• Collaborate closely with ML scientists and research engineers to design data representations, storage layouts, and access patterns that enable efficient model training and experimentation.
• Build and operate workflow orchestration for both production pipelines and large-scale batch jobs.
• Ensure infrastructure meets security, audit, and governance requirements.
• Champion engineering best practices across the data platform.
• Contribute to architecture decisions across the broader ML platform, including how compute, data, and training systems integrate.

Professionally, you will have

• A degree in Computer Science, Engineering, or a related quantitative discipline; significant industry experience in data engineering, MLOps, or data platform roles.
• Excellent Python engineering skills.
• Deep experience with cloud-native data infrastructure (AWS S3 / GCS, plus the surrounding ecosystem) and Infrastructure-as-Code (Terraform or equivalent).
• A track record of designing data pipelines and storage layouts for large, heterogeneous datasets.
• Experience building scalable analytical data processing workflows using modern engines and frameworks (e.g. Spark, Polars, Dask, DuckDB, or equivalent), with an understanding of their performance and architectural trade-offs.
• Hands-on experience with workflow orchestration (e.g. Airflow, Dagster, Prefect, or equivalent) and containerised environments (e.g. docker, k8s).
• Working knowledge of modern columnar / scientific data formats (Parquet, Zarr, TileDB, HDF5) and lakehouse technologies.
• Experience partnering closely with scientific or research users and comfortable with the messiness of real-world experimental data.
• Bonus experience: biomedical or genomics data (BAM, FASTQ, AnnData, OME-Zarr); regulated or pharma-partnered environments; data governance, FAIR principles, or research data management; feature store implementations.

Personally, you

• Are comfortable working in a matrixed environment, balancing multiple stakeholders and contributing effectively across teams.
• Take ownership of your work, proactively seek opportunities to contribute, and enable others to do their best work.
• Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect.
• Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams.
• Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes.

Working style & culture at Relation

At Relation, we operate in a matrixed, interdisciplinary environment, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together! The patient is waiting!