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# engineer python sql machine learning architecture

- **Company:** Spotify
- **Location:** UK (Remote available)
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Data Analysis, BigQuery, Information Engineering, Dataspaces, Data Systems, Distributed Data Store, Data Flow Control, Python (Programming Language), Machine Learning, Meta-Data Management, Cloud Services, SQL Databases, Data Streaming, Technical Debt
- **Published:** July 28, 2026
- **Apply:** https://www.workingnomads.com/job/go/1757155/

## About the Role

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

## Description

We're looking for a Staff Data Engineer who combines deep technical expertise with strategic thinking. You'll help define the long-term direction of our data ecosystem, influence engineering across multiple teams, and build the foundations that will scale with Spotify's future.

What You'll Do

 * 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.


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