Spotify - Senior Data Engineer - Partner & Platform Experience
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Requirements
• You have strong experience building and maintaining scalable data pipelines in production environments. • You are comfortable working with data modeling, transformation, testing, and defining metrics. • You have experience with SQL and programming languages such as Scala. • You’ve worked with modern data tooling (for example dbt or similar frameworks). • You understand cloud-based data platforms (ideally GCP) and the trade-offs between performance, cost, and scalability. • You care about data quality, governance, and building systems that are reliable and easy to maintain. • You're curious about the business and product reality behind the data - you want to understand why things are built, not just how. • You collaborate well with cross-functional partners and can explain technical concepts clearly. • You’re comfortable navigating ambiguity and finding practical solutions to open-ended problems. • You enjoy breadth and variety in your work and may have a background in engineering consulting, or similar. • Where You'll Be • This role is based in London or Stockholm. • 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. • At Spotify, we are passionate about inclusivity and making sure our entire recruitment process is accessible to everyone. We have ways to request reasonable accommodations during the interview process and help assist in what you need. If you need accommodations at any stage of the application or interview process, please let us know - we’re here to support you in any way we can.
Responsibilities
• Build and evolve high-impact data products e.g. pipeline and critical datasets that help teams understand user experience across platforms like desktop, TV, and partner integrations. • Contribute to the team's growing use of AI and agentic tooling to improve how we build and operate data systems. • Design and maintain scalable data pipelines and models that power analytics, experimentation, and data science. • Improve data systems by setting strong engineering standards for performance, quality, and reliability. • Develop tooling and automation to ensure accurate, efficient, and trustworthy data. • Support and mentor other engineers through code reviews, design discussions and knowledge sharing. • Work closely with data scientists, product managers, and engineers to solve complex problems and shape data-driven decisions.
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