Embedded Data Engineer, ML Pipelines on AWS

Remote Worker LTD.; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Private healthcare

Dental insurance

Work from abroad policy

Share purchase plan

EV scheme

Festive time off

Family-friendly benefits

Job summary

Trainline is seeking an Embedded Data Engineer in ML to design scalable data pipelines, feature stores and data models that power analytics and ML workloads within the ML domain. You will deploy cloud-native data apps on AWS, automate CI/CD, and collaborate with ML Engineers and Data Scientists to deliver reliable datasets and robust data platforms.

The role sits in the ML team with access to broader Data Engineering community, requiring Python/SQL, Spark, Airflow, and cloud data warehouse

Qualifications

• Proven ability to design data pipelines for analytics and ML workloads.
• Experience building data marts/warehouses in the cloud.

Responsibilities

• Design and build scalable data pipelines, data models, and feature stores for analytics and ML workloads.
• Deploy and maintain cloud-native data applications on AWS with CI/CD.
• Ensure production data pipelines are reliable with observability and engineering best practices.
• Collaborate with ML Engineers and Data Scientists to deliver well-structured datasets for ML use cases.
• Share knowledge across Data Engineering, Data Platform, and analytics communities.

Skills

Python

SQL

Data pipelines

Feature stores

Data modelling

Spark

Airflow

AWS

CI/CD

Jenkins

GitHub Actions

Parquet

Iceberg

Terraform

Docker

Ray

Tools

Spark

Airflow

AWS

Terraform

Docker

Job description

Trainline is seeking an Embedded Data Engineer in ML to design scalable data pipelines, feature stores and data models that power analytics and ML workloads within the ML domain. You will deploy cloud-native data apps on AWS, automate CI/CD, and collaborate with ML Engineers and Data Scientists to deliver reliable datasets and robust data platforms.

The role sits in the ML team with access to broader Data Engineering community, requiring Python/SQL, Spark, Airflow, and cloud data warehouse