Embedded Data Engineer

Who you are

• Working knowledge of Python and SQL
• Experience building data pipelines for downstream machine learning workloads, including feature engineering and model training workflows
• Comfort with data modelling and building efficient data marts and warehouses in the cloud
• Experience building data pipelines using tools such as Spark and Airflow, or similar technologies, within a cloud environment such as AWS
• Familiarity with both real-time and batch data workloads, along with modern data transformation and orchestration patterns
• Ideally, you may also have experience with parallel or distributed training frameworks such as Ray, or with modern data formats such as Parquet and Iceberg
• It would also be helpful if you have some experience with Infrastructure as Code (Terraform) and containerisation (Docker) to support automated, standardised deployments
• You may also have contributed to or maintained CI/CD pipelines (such as Jenkins or GitHub Actions) as part of production grade data systems, and enjoy solving complex data problems collaboratively

What the job involves

• At the heart of our Data and ML teams, embedded Data Engineers create the pipelines and tables that power business critical dashboards, enable self-service analytics, and fuel advanced machine learning models and real-time data products
• Working with tools like DBT, Spark, and Airflow, you'll transform high volume raw event data into user-friendly, high impact datasets that support machine learning use cases across the business
• As an Embedded Data Engineer in ML, you'll sit within the Machine Learning team, working day to day with Machine Learning Engineers and Data Scientists to build reliable datasets for ML use cases
• You'll also have access to Trainline's wider Data Engineering, Data Platform, and analytics community, working alongside other embedded Data Engineers in ML, including senior and principal engineers
• Design and build scalable data pipelines, data models, and feature stores that support analytics and machine learning workloads within the ML domain
• Deploy and maintain cloud-native data applications on AWS, using CI/CD pipelines to automate builds, testing, and releases
• Maintain the technical quality, performance, and reliability of production data pipelines through strong observability and engineering best practices
• Collaborate closely with Machine Learning Engineers and Data Scientists to build reliable, well-structured datasets that power ML use cases
• Work with the wider Data Engineering, Data Platform, and analytics community to share knowledge and align on best practices across teams

Benefits

• Flexible working (60% min office attendance)
• Cycle to work scheme
• Discounted gym memberships through Wellhub
• Private Health and Dental Insurance
• Enhanced Pension (matched contributions)
• Income Protection
• Share Incentive Plan (buy one get one free)
• Learning budget
• Primary & Secondary Caregiver Leave
• Shared Parental Leave
• Enhanced sick pay
• Free Perkbox subscription
• Electric Vehicle Salary Sacrifice Scheme
• YellowNest - Subsidised Childcare Fees
• Work from Abroad