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On the Personalization Team, we seek to understand the world of music and podcasts better than anyone else so that we can make great recommendations to every individual person and keep the world listening. Everyday, hundreds of millions of people all over the world use the products we build which include destinations like “Home” and “Search” as well as original playlists such as "Discover Weekly" and “Daily Mix.” We're a team of technologists, product insight experts, designers, and product managers in Boston, New York, Stockholm, and London.
As a Backend Engineer with data engineering experience, you will closely collaborate with our internal customers to determine the best API and backend system design to meet their scalability needs. In this role, you’ll help craft our data strategy and build the systems that power smarter, more intuitive features across Spotify. You'll bring your expertise in building scalable, reliable backend services while also applying modern data engineering practices—such as data quality, monitoring, and reproducibility—to support ML-powered recommendations and user-facing experiences. Above all, your work will impact the way the world experiences music.
What You'll Do
- Architect, develop, and operate scalable and highly available distributed systems.
- Collaborate with internal stakeholders to design scalable backend systems and data pipelines that power ML-based music recommendations and personalized experiences.
- Partner with ML, Data, and Backend Engineers to improve the reliability, observability, and efficiency of our data and backend stack.
- Advocate for modern engineering practices—e.g., data quality, monitoring, reproducibility—across the team and broader org.
- Contribute to strategic decisions around data architecture and technical direction.
- Work on what you want during regular hack days and bi-annual hack weeks.
Who You Are
- You have proven experience in backend and data engineering, including building scalable services, reliable data pipelines, and robust data platforms.
- You possess a strong understanding of data systems, distributed systems, and cloud-based infrastructure (e.g., Google Cloud Platform), and are comfortable working with large datasets using tools such as SQL and BigQuery.
- You collaborate effectively with ML, Data, and Backend teams, and understand the challenges of supporting ML models in production environments.
- You take initiative to improve systems, mentor peers across disciplines, and drive technical decisions independently.
- You prioritize code quality, data observability, and monitoring, and have experience working directly with stakeholders to build scalable, reusable APIs and systems.
- You thrive in fast-paced environments that value experimentation, iteration, and continuous learning.
Where You'll Be
- We offer you the flexibility to work where you work best! For this role, you can be within the North America region as long as we have a work location.
- This team operates within the Eastern Standard time zone for collaboration.
The United States base range for this position is $125,300 - $179,000 plus equity. The benefits available for this position include health insurance, six month paid parental leave, 401(k) retirement plan, monthly meal allowance, 23 paid days off, 13 paid flexible holidays. This range encompasses multiple levels. Leveling is determined during the interview process. Placement in a level depends on relevant work history and interview performance. These ranges may be modified in the future.
Spotify is an equal opportunity employer. You are welcome at Spotify for who you are, no matter where you come from, what you look like, or what’s playing in your headphones. Our platform is for everyone, and so is our workplace. The more voices we have represented and amplified in our business, the more we will all thrive, contribute, and be forward-thinking! So bring us your personal experience, your perspectives, and your background. It’s in our differences that we will find the power to keep revolutionizing the way the world listens.
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.
Spotify transformed music listening forever when we launched in 2008. Our mission is to unlock the potential of human creativity by giving a million creative artists the opportunity to live off their art and billions of fans the chance to enjoy and be passionate about these creators. Everything we do is driven by our love for music and podcasting. Today, we are the world’s most popular audio streaming subscription service.
Spotify New York, New York, USA Office
4 World Trade Center, New York, NY, United States, 10007
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
