Staff Data Engineer, Subscriptions User Understanding

• Build the foundational data platform that helps Spotify understand conversion, retention, engagement, and subscriber health for more than 200 million Premium subscribers worldwide. • Help define the long-term direction of Spotify's data ecosystem, influence engineering across multiple teams, and build foundations that scale toward one billion users and $100 billion in revenue. • Combine deep technical expertise with strategic thinking in a role that spans analytics, experimentation, machine learning, and product development. • Be based anywhere in EMEA where Spotify has a work location (excluding France due to on-call restrictions).

• Enjoy solving broad, cross-cutting engineering challenges and bringing teams together around a shared technical vision. • Have extensive experience designing, building, and operating large-scale cloud-native data platforms. • Have deep experience building reliable distributed data pipelines using technologies such as Scio, BigQuery, Dataflow, GCS, SQL, Python, or similar cloud-native tools. • Have a strong understanding of modern data modelling, metadata management, governance, and data quality practices. • Know how to balance immediate business needs with long-term architectural sustainability. • Influence technical direction through strong communication, sound judgement, and collaboration rather than formal authority alone. • Communicate effectively with engineers, product managers, data scientists, analysts, and senior leadership. • Enjoy mentoring engineers and helping technical teams grow through coaching, technical guidance, and thoughtful feedback. • Be motivated by building platforms that directly influence Spotify's long-term subscriber growth and business success.

• Have experience designing data models and platform architecture that support sustainable growth at billion-user scale. • Have experience with streaming data platforms alongside batch processing. • Have experience leading architectural decisions and establishing engineering best practices across multiple squads. • Have experience simplifying complex systems and reducing technical debt across a broad data ecosystem.

• Define and drive the long-term data engineering strategy for the Subscriptions User Understanding domain, identifying opportunities that improve scalability, reliability, and business impact. • Design scalable batch and streaming data platforms that power analytics, experimentation, machine learning, and subscriber experiences. • Partner with Product, Engineering, Data Science, Analytics, and Platform teams to turn complex business challenges into durable, well-designed data solutions. • Lead architectural decisions and establish engineering best practices for data quality, governance, observability, reliability, and operational excellence across multiple squads. • Design data models and platform architecture that support sustainable growth toward one billion users while balancing performance, cost, and maintainability. • Simplify complex systems, reduce technical debt, and improve the developer experience across the broader data ecosystem. • Mentor senior engineers, influence technical direction through collaboration, and help foster a culture of thoughtful engineering, knowledge sharing, and continuous improvement.

• Work remotely from anywhere in EMEA where Spotify has a work location (excluding France). • Collaborate within Central European and GMT time zones. • Receive extensive learning opportunities through Spotify's dedicated GreenHouse team. • Receive flexible share incentives letting you choose how you share in Spotify's success. • Receive six months of global parental leave for all new parents. • Access All The Feels, the employee assistance programme and self-care hub. • Swap public holidays flexibly according to your values and beliefs.

Spotify is the world's most popular audio streaming subscription service with more than 700 million monthly active users and over 200 million Premium subscribers. The Subscriptions User Understanding team builds the foundational data platform that enables better decisions across analytics, experimentation, machine learning, and product development.