Embedded Data Engineer – ML

Overview

As Embedded Data Engineer in ML, you design scalable data pipelines and models to power analytics and ML workflows. You deploy cloud-native data apps on AWS and maintain production reliability with observability. You collaborate with ML Engineers and Data Scientists to deliver well-structured datasets and share best practices across the data community. This role sits at the intersection of data engineering and ML, enabling real-time analytics and ML-powered decisions at scale.

Pay / Benefits

• private healthcare & dental insurance
• work from abroad policy
• 2-for-1 share purchase plans
• EV Scheme to reduce carbon emissions
• extra festive time off
• family-friendly benefits

Responsibilities

• Design and build scalable data pipelines, data models, and feature stores for analytics and ML workloads in the ML domain
• Deploy and maintain cloud-native data applications on AWS with CI/CD automation
• Maintain production data pipeline quality, performance, and reliability through observability and best practices
• Collaborate with ML Engineers and Data Scientists to create reliable, well-structured datasets for ML use cases
• Engage with the wider Data Engineering, Data Platform, and analytics community to share knowledge and align on best practices

Key requirements

• Python and SQL proficiency
• Experience building data pipelines for downstream ML workloads (feature engineering and model training workflows)
• Cloud data modelling and data marts/warehouses in AWS
• Data pipelines with Spark and Airflow (or similar) in a cloud environment
• Experience with real-time and batch data workloads and modern transformation/orchestration patterns
• Knowledge of Ray (optional) and modern data formats like Parquet and Iceberg
• Infra as Code and containerisation (Terraform, Docker) is helpful
• CI/CD experience (Jenkins or GitHub Actions) for production data systems
• Strong collaborative problem-solving skills

• collaboration
• problem-solving
• communication
• Python
• SQL
• Spark

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