Spotify - Data Analyst II – Performance Optimization Squad
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Requirements
• You have experience working with infrastructure, platform, or cloud cost data; Kubernetes metrics, cost attribution, utilization signals, or observability data feel familiar • Or you're a strong technical analyst with enough grounding in distributed systems and cloud infrastructure to navigate GKE cost data, JVM metrics, and resource utilization signals • You write clean, efficient SQL and Python; comfortable enough to model data and build lightweight pipelines, not just query existing tables • You're self-directed: at your best when hunting for problems in the data, not waiting to be handed them • Comfortable with ambiguity and able to carve your own path in an early-stage, unstructured environment • Experienced with data visualization tools (Looker or similar) and know how to make a dashboard tell a story, not just display numbers • You communicate clearly and confidently with engineers and non-technical stakeholders alike • Where You'll Be • This role is based in Stockholm or London. • 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
• Optimization at this scale is only possible when someone can see the problem clearly. You'll build the data foundation from scratch; designing and owning the datasets, pipelines, and metrics that make performance inefficiencies visible across the platform. • Design, build, and maintain datasets and data pipelines that surface resource utilization, cost, and performance signals across Spotify's infrastructure • Define and own metrics for efficiency, latency, and resource utilization; turning raw infrastructure signals into insights that drive prioritization • Proactively investigate performance data to surface optimization opportunities, not just respond to engineering requests • Build dashboards and analyses that support decision-making across the squad and partnering platform teams • Work with engineers and platform teams to define guardrail metrics, validate findings, and measure the real-world impact of optimization efforts • Translate complex infrastructure data into clear stories for both technical and non-technical audiences • Own the data foundation: There is no inherited data infrastructure here; you'll design and build it from scratch. What gets measured, and how, is yours to define • See your impact directly: Every insight you surface translates into cost savings. We measure success in dollars saved and efficiency gained; your work shows up in production • Breadth at scale: Work across the entire Spotify platform; 3,200+ microservices, 40,000 VMs, 500,000 K8s pods. Few companies offer data problems at this scale • Greenfield from day one: Help shape the culture, tooling, and data strategy of a brand new squad with strong executive support
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