GCP Data Engineer

Hybrid in London·Added 2 months ago (last checked 1 Nov)

Agency

Harnham

243 open roles

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Our summary

Salary: £700 - £750 a day
Work mode: Hybrid, London
Contract: Contract role, inside IR35
Experience: 5+ years

Skills

Nice to have

Requirements

What we're looking for

Strong Python development experience
Hands-on experience with cloud object storage for unstructured data
(Google Cloud Storage preferred; AWS S3 also acceptable)
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

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

Pay & benefits

What you'll get

£700 - £750 per day inside IR35

The role

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 Google Cloud, supporting ML teams by enabling efficient data loading, dataset management, and cloud-based training workflows.

You'll work closely with ML engineers and researchers, ensuring that large volumes of unstructured and structured data can be reliably accessed, processed, and consumed by PyTorch-based systems.

What you'll do

Design and build cloud-native data pipelines using Python on GCP
Manage large-scale object storage for unstructured data (Google Cloud Storage preferred)
Support PyTorch-based workflows, particularly around data loading and dataset management in the cloud
Build and optimise data integrations with BigQuery and SQL databases
Ensure efficient memory usage and performance when handling large datasets
Collaborate with ML engineers to support training and experimentation pipelines (without owning model development)
Implement monitoring, testing, and documentation to ensure production-grade reliability
Participate in agile ceremonies, code reviews, and technical design discussions