Data / ML Engineer

As a Data / ML Engineer, you’ll combine hands-on data analysis, data engineering and applied data science to build and improve production ML solutions. You’ll work closely with Product Owners to understand what they want to learn or improve, explore the data and propose experiments. You’ll also collaborate with Data Scientists and Engineers to build and maintain the pipelines that support these solutions in production.

• Analyse data to answer product questions. Explore datasets, identify trends and anomalies, and communicate findings that help Product Owners make decisions.
• Work with Product Owners to shape experiments. Clarify requirements, translate product questions into testable hypotheses, suggest experiments and help define how to measure success and interpret results.
• Build and maintain end-to-end ML pipelines. Cover ETL, feature engineering, model training, deployment, batch scoring, API-based inference and monitoring.
• Work with our data and ML stack. Use AWS SageMaker, AWS Glue, AWS Lambda, MLflow, Spark and Airflow, with Great Expectations for data quality where appropriate.
• Collaborate with Data Scientists and Engineers. Improve how models are developed, deployed and operated in production. Work in an Agile Scrum environment and apply practices such as TDD and DDD where appropriate.

• Hands-on experience across data engineering and applied data science, with the ability to move between exploratory analysis, experimentation and production pipeline development.
• Strong data analysis skills, including the ability to investigate datasets, interpret findings and explain what they mean for product decisions.
• Experience working directly with Product Owners to understand requirements, propose experiments and define success metrics.
• Strong experience building end-to-end production ML pipelines, covering ETL, feature engineering, model training, deployment, serving and monitoring.
• Proficiency with AWS SageMaker, AWS Glue and AWS Lambda, plus hands-on experience with MLflow, Spark and Airflow.
• Clear communication and effective collaboration with Product Owners, Data Scientists and Engineers.

• Experience with Google Vertex AI.
• Familiarity with the Great Expectations Framework for data quality.
• Proven experience working in Agile Scrum environments and practising TDD and DDD.

• Be part of our collegial environment where responsibility and authority are shared equally amongst colleagues and help create our company culture
• A culture in which we don’t criticise failure but ensure we learn from our mistakes
• An Agile environment where your ideas are welcome
• The possibility to grow and experience different projects
• Ongoing Training & Mentoring
• The possibility to travel