In the Data Processing Product Area, we build the platforms and tools that power how Spotify processes, manages, and consumes data at scale. Our work enables teams across Spotify to solve complex data challenges with confidence. We focus on improving efficiency, quality, and innovation—helping engineers and data practitioners move faster while maintaining reliability and cost awareness.
What You'll Do
Collaborate with product and engineering teams to build and operate infrastructure that enables reliable, scalable data processing
Design and develop tools that improve developer productivity and reduce the overhead of working with data pipelines
Contribute to platform reliability, performance, and cost efficiency across large-scale data systems
Partner with data experts and squads across Spotify to evolve best practices, standards, and tooling
Support the adoption and migration of data pipelines onto new platform capabilities
Help shape the future of Spotify’s data ecosystem through continuous improvement and experimentation
Who You Are
You have experience building backend systems using Java
You are familiar with platform engineering concepts and working in data-intensive environments
You are comfortable working with large-scale data using SQL and modern analytics platforms such as BigQuery
You have experience with distributed data processing frameworks such as Spark, Flink, Beam, or similar
You are experienced with cloud infrastructure, containerized applications, and DevOps practices
You have working knowledge of Kubernetes and its core concepts
You have experience building or maintaining data pipelines using Scala and/or Python
You care about code quality, testing, and delivering reliable systems
You enjoy working in collaborative environments and navigating open-ended problems
You are curious, experiment-driven, and motivated by using data to inform decisions
Where You'll Be
This role is based in London
We offer you the flexibility to work where you work best! There will be some in person meetings, but still allows for flexibility to work from home
