Data Engineer – MLOps & AWS

About the role

We are looking for a seasoned Data Engineer to join our team and drive the end‑to‑end machine‑learning lifecycle. You will collaborate closely with quantitative analysts and data scientists to build robust, production‑ready pipelines that enable rapid model development and deployment.

Key responsibilities

• Design, implement and maintain MLOps pipelines for feature preparation, model training, tuning and inference.
• Develop and optimise data workflows on AWS services such as S3, Step Functions and Athena.
• Integrate Databricks notebooks, feature stores and model registries (MLflow) into production environments.
• Partner with quants and data scientists to translate analytical requirements into scalable engineering solutions.
• Ensure data quality, reliability and performance across large‑scale big‑data architectures.

Required profile

• Strong background in Python development with at least 8‑10 years of experience.
• Deep understanding of MLOps concepts and the machine‑learning model lifecycle.
• Proven ability to work in cross‑functional teams and solve complex data engineering challenges.
• Experience with big‑data technologies and architecture design.

Required skills

• Python (version 3)
• AWS ecosystem – S3, Step Functions, Athena
• Databricks
• Feature Stores
• MLflow (model registry)
• Big Data Architecture & Technologies
• Machine Learning pipelines