Software Engineer, Data Platform (Hybrid, London)
Hlx Life Sciences is recruiting on behalf of our client, a UK-based biotechnology company using advanced immune profiling, data engineering and machine learning to understand human health and support the development of precision medicines.
About the role
Our client is applying cutting-edge immune system science, data engineering and machine learning to understand human health in a deeper and more actionable way. As their work moves closer to the clinic, they are building the data infrastructure needed to make complex biological and clinical data reliable, reproducible, governed and ready for scientific discovery.
They are looking for a Software Engineer to help build and operate the platform behind that work. They treat the data platform as a product, not just infrastructure: it should be reliable, usable, well-documented and shaped around the needs of the scientists, data scientists and engineers who depend on it.
What you will do
- Build, maintain and improve cloud-native data platform services used by scientific, data science and engineering teams
- Develop and operate data lake and medallion-style architectures across raw, cleaned, curated and analysis-ready data layers
- Create reliable patterns for data ingestion, validation, transformation, storage, metadata capture and access control
- Support reproducible scientific and data pipelines, including helping migrate workflows into production-ready deployable artefacts
- Improve data discoverability, lineage, provenance, auditability and reuse in line with practical FAIR data principles
- Build automation, infrastructure and developer workflows using infrastructure-as-code, CI/CD, containers and cloud-native practices
- Work closely with scientists, software engineers and platform users to turn real data problems into reliable platform capabilities
- Improve reliability, documentation, maintainability and cost-awareness across data platform services and pipelines
What we are looking for
Core experience
Our client doesn’t expect every candidate to have used every tool in their stack. At CV stage they are mainly looking for evidence of strong engineering judgement and hands‑on experience in a few key areas:
- Strong software engineering experience in Python or another production‑grade language
- Practical experience building and operating cloud‑native systems on AWS
- Experience with infrastructure‑as‑code, ideally Pulumi, Terraform or an equivalent tool
- Experience with data lakes, medallion architectures, lakehouse patterns or large‑scale analytical data platforms
- Experience enabling, operating or productionising data pipelines or scientific workflows
Technologies used or valued highly
Depth and judgement matter more than superficial exposure to every tool:
- AWS
- Pulumi, Terraform or equivalent infrastructure‑as‑code
- GitHub Actions or equivalent CI/CD
- SQLMesh or equivalent SQL‑based transformation, modelling, testing and deployment framework
- Containers and modern DevOps practices
- Data lakes, object storage, metadata, validation, schema management and data lifecycle patterns
- FAIR data principles, lineage, provenance and governance
Nice to have
- Seqera Platform and Nextflow for reproducible scientific workflow execution
- Dagster or Snowflake
- Experience in scientific, bioinformatics, computational biology, immunology, clinical, healthcare or other regulated data environments
- Lakehouse technologies such as Delta Lake, Apache Iceberg, Apache Hudi, Databricks, Athena, Glue, Trino, Spark or DuckDB
- Regulated software, security, quality or healthcare frameworks such as IEC 62304, ISO 27001, ISO 13485, HTA, HIPAA or similar
- Experience building self‑service data platforms, internal developer platforms or platform capabilities for data science teams
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