Harnham
GCP Data Engineer
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GCP Data Engineer
£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.
The Role
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.
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Key Responsibilities
- 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 datasets
- Implement monitoring, testing, and documentation to ensure production-grade reliability
- Participate in agile ceremonies, code reviews, and technical design discussions
Tech Stack & Experience
Must Have
- 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
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Nice to Have
- 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
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