This is an opportunity to join a fast scaling, digital first consumer platform at a pivotal point in its data journey. They are investing heavily in their data infrastructure and are building a high performing, software led data engineering team to support real time, high volume analytics across the business. You will have genuine influence over how data engineering is done, working on modern tooling and best practice from day one.

The Company

They are a UK based, consumer facing digital platform operating in a highly regulated online environment. Since launching less than a decade ago, the business has grown rapidly, supported by significant recent funding and a strong focus on product, user experience and responsible engagement. Data is central to their growth strategy, and they are building a modern, scalable analytics platform to support this ambition. The team operates fully remotely across the UK and Europe.

The Role

• Design, build and own end to end data pipelines supporting real time and batch analytics
• Work on high volume, low latency data use cases with a strong focus on reliability and scalability
• Develop production grade data solutions using software engineering best practices such as CI/CD, testing and version control
• Contribute to the design of data models and analytics layers used across the business
• Help implement and mature staging, testing and deployment processes across the data platform
• Collaborate closely with analytics, product and engineering teams to enable better decision making

Your Skills and Experience

• Strong commercial experience as a data engineer in modern, cloud based environments
• Advanced Python and SQL, with experience using PySpark or similar distributed processing frameworks
• Experience building and maintaining data pipelines in AWS using services such as S3, Glue or EKS
• Hands on experience with modern analytics stacks such as Snowflake, dbt and Iceberg
• Familiarity with orchestration tools such as Dagster or Airflow
• Strong understanding of data modelling concepts and analytics best practice
• A software engineering mindset, including Git, testing and CI/CD workflows