Description:
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