Job Description

£500 - £550 per day inside IR35

6-month contract

Hybrid working in London

We're working with a global healthcare and AI research organisation at the forefront of applying data engineering and machine learning to accelerate scientific discovery. Their work supports large-scale, domain-specific datasets that power research into life-changing treatments.

They're now looking for a GCP Data Engineer to join a multidisciplinary team responsible for building and operating robust, cloud-native data infrastructure that supports ML workloads, particularly PyTorch-based pipelines.

You'll focus on designing, building, and maintaining scalable data pipelines and storage systems in BigQuery, supporting ML teams by enabling efficient data loading, dataset management, and cloud-based training workflows.

• Design and build cloud-native data pipelines using Python on GCP

• Manage large-scale object storage for unstructured data within BigQuery

• Build and optimise data integrations with BigQuery and SQL databases

• Ensure efficient memory usage and performance when handling large datasetsImplement monitoring, testing, and documentation to ensure production-grade reliability

• Participate in agile ceremonies, code reviews, and technical design discussions

• Strong Python development experience

• Hands-on experience with cloud object storage for unstructured data within BigQuery

• PyTorch experience, particularly:

  • Dataset management

  • Data loading pipelines

  • Running PyTorch workloads in cloud environments We are not looking for years of PyTorch experience - one or two substantial 6-12 month projects is ideal

• 5+ years cloud experience, ideally working with large numbers of files in cloud buckets

• Experience with additional GCP services, such as:

  • Cloud Run

  • Cloud SQL

  • Cloud Scheduler

• Exposure to machine learning workflows (not ML engineering)

• Some pharma or life sciences experience, or a genuine interest in working with domain-specific scientific data

Please send your CV