At a Glance
- Tasks: Design and build data pipelines to optimise performance across Spotify's infrastructure.
- Company: Join a leading tech company with a focus on innovation and collaboration.
- Benefits: Flexible work environment, competitive salary, and opportunities for professional growth.
- Other info: Be part of a new squad shaping the future of data strategy at Spotify.
- Why this job: Make a real impact by turning data into insights that drive cost savings.
- Qualifications: Experience with SQL, Python, and data visualisation tools; strong analytical skills required.
The predicted salary is between 60000 - 80000 € per year.
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.
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.
What the job involves
- 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.
Data Analyst (Performance Optimization Squad) in London employer: Deepstreamtech
At Spotify, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. As a Data Analyst in our Performance Optimization Squad, you'll enjoy the flexibility to work where you feel most productive, while also having the opportunity to shape the data strategy from the ground up. With a focus on employee growth and impactful work, your contributions will directly translate into cost savings and efficiency gains across our expansive platform.
StudySmarter Expert Advice🤫
We think this is how you could land Data Analyst (Performance Optimization Squad) in London
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or at events. Ask them about their experiences and the company culture. This can give you insider info and might even lead to a referral!
✨Tip Number 2
Prepare for those interviews! Brush up on your SQL and Python skills, and be ready to discuss how you've tackled data problems in the past. Practice explaining complex data concepts in simple terms – it’ll impress both technical and non-technical folks.
✨Tip Number 3
Show off your data visualisation skills! Create a portfolio of dashboards or analyses that tell a story. This will not only demonstrate your technical abilities but also your knack for making data accessible and actionable.
✨Tip Number 4
Don’t just apply anywhere; apply through our website! Tailor your application to highlight your experience with cloud cost data and performance optimisation. We love seeing candidates who are genuinely interested in what we do!
We think you need these skills to ace Data Analyst (Performance Optimization Squad) in London
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your experience with SQL and Python in your application. We want to see how you've used these skills to model data and build pipelines, so don’t hold back on the details!
Tell a Story with Your Data:When discussing your past projects, focus on how you turned raw data into actionable insights. We love candidates who can communicate complex information clearly, so think about how you can showcase this in your application.
Embrace the Ambiguity:We’re looking for self-directed individuals who thrive in unstructured environments. In your application, share examples of how you've navigated ambiguity and carved your own path to solve problems.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves.
How to prepare for a job interview at Deepstreamtech
✨Know Your Data Inside Out
Make sure you’re familiar with the types of data you'll be working with, like Kubernetes metrics and cloud cost data. Brush up on your SQL and Python skills, as you'll need to demonstrate your ability to model data and build pipelines during the interview.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've proactively hunted for problems in data rather than waiting for them to be handed to you. Think about specific instances where you identified performance inefficiencies and how you tackled them.
✨Communicate Clearly and Confidently
Practice explaining complex technical concepts in simple terms. You’ll need to communicate effectively with both engineers and non-technical stakeholders, so being able to tell a clear story with your data is crucial.
✨Demonstrate Your Flexibility and Initiative
Since this role involves building the data foundation from scratch, show that you're comfortable with ambiguity and can carve your own path. Share experiences where you thrived in unstructured environments and took the initiative to drive projects forward.
