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# GCP Data Engineer

- **Company:** Harnham
- **Location:** London, UK
- **Experience:** Starter
- **Salary:** £182,000.0 - £195,000.0
- **Contract:** Temporary contract
- **Skills:** Amazon S3, Big Data, BigQuery, Cloud Computing, Code Review, Data Files, Extract Transform Load (ETL), Python (Programming Language), Machine Learning, Cloud Services, SQL Databases, Unstructured Data, Google Cloud, Pytorch, Build Management, Machine Learning Operations, Data Pipelines
- **Published:** September 14, 2026
- **Apply:** https://www.reed.co.uk/jobs/gcp-data-engineer/57341158

## About the Role

* 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, * engineer
 * gcp
 * Pytorch
 * GKS
 * cloudrun

## Description

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 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., * 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


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