Data Scientist II – Performance Optimization Squad
Spotify - Stockholm
Posted Mar 27, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Data Scientist II – Performance Optimization Squad Stockholm The Performance Optimization Squad is a newly formed team in the Core Infrastructure Studio with a mission to establish a core competency in performance engineering and address systemic inefficiencies across Spotify's platform. With 3,200+ microservices, 40,000 VMs at peak, and 500,000 K8s pods, even minor fleet-wide efficiency improvements result in substantial cost savings. We've already identified $8M+ in annual savings from our first initiative alone, and we're just getting started. This squad works horizontally across the entire stack - but none of that optimization happens without the data to see it. We're looking for a Data Analyst who will build the measurement foundation that drives every decision we make. The Performance Optimization Squad is a newly formed team in the Core Infrastructure Studio with a mission to establish a core competency in performance engineering and address systemic inefficiencies across Spotify's platform. With 3,200+ microservices, 40,000 VMs at peak, and 500,000 K8s pods, even minor fleet-wide efficiency improvements result in substantial cost savings. We've already identified $8M+ in annual savings from our first initiative alone, and we're just getting started. This squad works horizontally across the entire stack - but none of that optimization happens without the data to see it. We're looking for a Data Analyst who will build the measurement foundation that drives every decision we make. What You'll Do Optimization at this scale is only possible when someone can see the problem clearly. You'll build the data foundation from scratch;
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