Data Engineer

Data Engineer - Contract Duration: 6 months Location: Central London - 2/3 days per week in the office. Rate: £750 Inside IR35 I'm partnering with a technology-led organisation to hire a Senior Data Engineer with strong cloud and Python experience and hands-on exposure to PyTorch-based ML workloads. This role sits firmly in data engineering rather than data science or pure ML engineering and will suit someone who has supported ML pipelines and datasets in production cloud environments. You'll work on large-scale, cloud-based data systems, managing high volumes of unstructured data and enabling machine learning workflows to run reliably and efficiently. The focus is on robust engineering, not experimentation. Key Responsibilities • Design, build, and maintain scalable cloud data pipelines supporting ML workloads • Manage large volumes of unstructured data using cloud object storage (GCS / S3) • Support PyTorch-based data loading and dataset management in production environments • Work closely with ML practitioners to enable training and inference pipelines • Ensure efficient memory usage and performance when handling large datasets • Integrate data from SQL-based systems into cloud and ML pipelines • Apply best practices around reliability, monitoring, and scalability Required Experience • Strong commercial experience as a Data Engineer • Strong Python development skills • Hands-on experience with cloud object storage (GCS preferred, or AWS S3) • Practical PyTorch experience (e.g. supporting training pipelines, dataset handling, data loaders) • Experience working in cloud environments with large-scale file-based data Desired Experience • BigQuery (GCP) • SQL databases (Microsoft SQL Server preferred; PostgreSQL also acceptable) • Memory management and performance optimisation • Exposure to ML workflows (without being a dedicated ML Engineer) Nice to Have • Broader GCP experience (Cloud Run, Cloud SQL, Cloud Scheduler, etc.) • Pharma or life sciences domain exposure (or strong interest in the space) • TensorFlow experience (acceptable alternative to PyTorch) This is an excellent opportunity for a Data Engineer who has worked alongside ML teams and understands how to operationalise PyTorch workloads in the cloud. Apply below! Desired Skills and Experience Data Engineer - Contract Duration: 6 months Location: Central London - 2/3 days per week in the office. Rate: £750 Inside IR35 I'm partnering with a technology-led organisation to hire a Senior Data Engineer with strong cloud and Python experience and hands-on exposure to PyTorch-based ML workloads. This role sits firmly in data engineering rather than data science or pure ML engineering and will suit someone who has supported ML pipelines and datasets in production cloud environments. You'll work on large-scale, cloud-based data systems, managing high volumes of unstructured data and enabling machine learning workflows to run reliably and efficiently. The focus is on robust engineering, not experimentation. Key Responsibilities • Design, build, and maintain scalable cloud data pipelines supporting ML workloads • Manage large volumes of unstructured data using cloud object storage (GCS / S3) • Support PyTorch-based data loading and dataset management in production environments • Work closely with ML practitioners to enable training and inference pipelines • Ensure efficient memory usage and performance when handling large datasets • Integrate data from SQL-based systems into cloud and ML pipelines • Apply best practices around reliability, monitoring, and scalability Required Experience • Strong commercial experience as a Data Engineer • Strong Python development skills • Hands-on experience with cloud object storage (GCS preferred, or AWS S3) • Practical PyTorch experience (e.g. supporting training pipelines, dataset handling, data loaders) • Experience working in cloud environments with large-scale file-based data Desired Experience • BigQuery (GCP) • SQL databases (Microsoft SQL Server preferred; PostgreSQL also acceptable) • Memory management and performance optimisation • Exposure to ML workflows (without being a dedicated ML Engineer) Nice to Have • Broader GCP experience (Cloud Run, Cloud SQL, Cloud Scheduler, etc.) • Pharma or life sciences domain exposure (or strong interest in the space) • TensorFlow experience (acceptable alternative to PyTorch) This is an excellent opportunity for a Data Engineer who has worked alongside ML teams and understands how to operationalise PyTorch workloads in the cloud. Apply below!