MLOps Data Engineer (GCP)

MLOps Data Engineer Hybrid 2/3 days – London Outside IR/35 We’re looking for a Data Engineer with strong MLOps ownership—someone who builds reliable data pipelines and designs, runs, and improves ML pipelines in production. You won’t be training models day-to-day like a Data Scientist; instead, you’ll enable Data Science by delivering high-quality datasets, reproducible training pipelines, robust deployments, and monitoring that keeps ML systems healthy and trustworthy. What you’ll do • Design, build, and operate scalable data pipelines for ingestion, transformation, and distribution • Develop and maintain ML pipelines end-to-end: data preparation, feature generation, training orchestration, packaging, deployment, and retraining • Partner closely with Data Scientists to productionize models: standardise workflows, ensure reproducibility, and reduce time-to-production • Build and maintain MLOps automation: CI/CD for ML, environment management, artefact handling, versioning of data/models/code • Implement observability for ML systems: monitoring, alerting, logging, dashboards, and incident response for data + model health • Establish best practices for data quality and ML quality: validation checks, pipeline tests, lineage, documentation, and SLAs/SLOs • Optimise cost and performance across data processing and training workflows (e.g., Spark tuning, BigQuery optimisation, compute autoscaling) • Ensure secure, compliant handling of data and models, including access controls, auditability, and governance practices What makes you a great fit • 4+ years of experience as a Data Engineer (or ML Platform / MLOps Engineer with strong DE foundations) shipping production pipelines • Strong Python and SQL skills; ability to write maintainable, testable, production-grade code • Solid understanding of MLOps fundamentals: model lifecycle, reproducibility, deployment patterns, and monitoring needs • Hands-on experience with orchestration and distributed processing in a cloud environment • Experience with data modelling and ETL/ELT patterns; ability to deliver analysis-ready datasets • Familiarity with containerization and deployment workflows (Docker, CI/CD, basic Kubernetes/serverless concepts) • Strong GCP experience and services such as Vertex, BigQuery, Composer, Dataproc, Cloud Run, Dataplex, Cloud Storage/or at least one major cloud provider, GCP, AWS, Azure • Strong troubleshooting mindset: ability to debug issues across data, infra, pipelines, and deployments Nice to have / big advantage • Experience with ML tooling such as MLflow (tracking/registry), Vertex AI / SageMaker / Azure ML, or similar platforms • Experience building and maintaining feature stores (e.g., Feast, Vertex Feature Store) • Experience with data/model validation tools (e.g., Great Expectations, TensorFlow Data Validation, Evidently) • Knowledge of model monitoring concepts: drift, data quality issues, performance degradation, bias checks, and alerting strategies • Infrastructure-as-Code (Terraform) and secrets management / IAM best practices • Familiarity with governance/compliance standards and audit requirements