Research Data Engineer
relationrx2 months ago
London, United KingdomSenior
Responsibilities
- Design, build, and maintain scalable data pipelines for multimodal scientific data.
- Optimize data movement, storage layouts, and access patterns for analytical and machine learning workloads.
- Build and evolve cloud-native data lake or lakehouse infrastructure.
- Implement data versioning, lineage, data quality monitoring, security, audit, and governance controls.
- Partner with data scientists, ML scientists, and research engineers to enable efficient data workflows and model training.
- Build and operate workflow orchestration for production pipelines and large-scale batch jobs.
- Contribute to architecture decisions integrating compute, data, and training systems across the ML platform.
- Champion engineering best practices across the data platform.
Requirements
- Degree in Computer Science, Engineering, or a related quantitative discipline, with significant industry experience in data engineering, MLOps, or data platform roles.
- Excellent Python engineering skills.
- Deep experience with cloud-native data infrastructure, including AWS S3 or GCS, and Infrastructure-as-Code such as Terraform.
- Experience designing data pipelines and storage layouts for large, heterogeneous datasets.
- Experience with scalable analytical data processing workflows using Spark, Polars, Dask, DuckDB, or equivalent technologies.
- Hands-on experience with workflow orchestration using Airflow, Dagster, Prefect, or equivalent tools.
- Experience with containerized environments such as Docker and Kubernetes.
- Working knowledge of Parquet, Zarr, TileDB, HDF5, and lakehouse technologies.
- Experience partnering with scientific or research users and working with real-world experimental data.
- Bonus experience with biomedical or genomics data such as BAM, FASTQ, AnnData, or OME-Zarr; regulated or pharma-partnered environments; data governance, FAIR principles, research data management; or feature store implementations.
- Ability to collaborate effectively across a matrixed, interdisciplinary environment and communicate constructively with stakeholders.
Benefits
- Work in interdisciplinary teams spanning biology, computation, engineering, scientific, technical, and operational domains.
- Work from Relation’s state-of-the-art wet and dry labs in central London.
- Inclusive and equal-opportunity workplace culture.
- Opportunity to contribute to transformational medicine discovery and research data infrastructure.
Tech Stack
Categories
Data Engineering