At a Glance
- Tasks: Build and maintain analytical data models and reliable data pipelines.
- Company: Join Spotify's innovative Platform team driving global growth.
- Benefits: Flexible work environment, competitive salary, and inclusive culture.
- Other info: Collaborative team atmosphere with opportunities for personal and professional growth.
- Why this job: Make a real impact on how Spotify scales and serves users worldwide.
- Qualifications: 2+ years in analytics engineering, strong SQL skills, and experience with dbt.
The predicted salary is between 55000 - 65000 € per year.
Mission Statement The Platform team creates the technology that enables Spotify to learn quickly and scale easily, enabling rapid growth in our users and our business around the globe. Spanning many disciplines, we work to make the business work; creating the infrastructure, tooling, frameworks, and capabilities needed to welcome a billion customers.
About the Team We’re looking for an Analytics Engineer II to join Spotify's Platform Central Data (PCD) squad, a cross-functional Data Engineering and Analytics Engineering team within the Platform Mission. You’ll help build and maintain trusted analytical models, metrics, and data products that power developer productivity, platform health, and leadership decision-making. Working closely with Data Engineers, Product, Engineering, and Platform partners, you’ll translate platform signals into reliable, well-modeled data assets that help Spotify ship faster and safer.
What You’ll Do
- Build and maintain analytical data models using dbt (or similar SQL-based transformation frameworks) in BigQuery for a broad set of stakeholders
- Build and operate reliable data pipelines using SQL, with a focus on testing, observability, and CI/CD
- Help define and evolve key metrics for platform health, developer productivity, and ML/AI platform adoption
- Partner with Data Engineers on upstream pipelines and collaborate with Product, Engineering, and Data Science to scope and deliver insights
- Improve data quality, performance, and cost efficiency across pipelines and models, including troubleshooting and backfills
- Contribute to dashboards and self-serve data products that enable better decision-making across teams
- Follow and contribute to data quality, testing, and documentation practices across the analytics layer
- Participate in a fair support rotation for key datasets, pipelines, and analytical products
Who You Are
- You have 2+ years of experience in analytics engineering, data engineering, or a related field
- You have strong SQL skills and experience with data modelling
- You are experienced with dbt (or similar SQL-based transformation frameworks) and a cloud data warehouse such as BigQuery, Snowflake, Redshift, or Databricks SQL
- You are familiar with workflow orchestration tools such as Airflow, Dagster, Prefect, or Flyte
- You care about data quality, reliability, and testability
- You are comfortable working with BI/visualisation tools such as Looker or Tableau
- You communicate clearly with both technical and non-technical partners
- You are able to prioritize and deliver in a fast-moving environment
- You have experience with platform or developer productivity data, experimentation, or ML/AI metrics
Where You'll Be This role is based in London or Stockholm. 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.
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.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Analytics Engineer II in London employer: Spotify
Spotify is an exceptional employer that fosters a culture of inclusivity and innovation, making it an ideal place for an Analytics Engineer II. With flexible working arrangements in vibrant locations like London and Stockholm, employees benefit from a collaborative environment that encourages personal growth and professional development. The company prioritises data quality and reliability while empowering its workforce to contribute to meaningful projects that shape the future of music streaming.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer II in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Spotify. A friendly chat can open doors and give you insights that job descriptions just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your analytical models and data pipelines. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for the interview by brushing up on SQL and dbt. Be ready to discuss your past projects and how you've tackled challenges in data quality and performance. Confidence is key!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining the Spotify family.
We think you need these skills to ace Analytics Engineer II in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Analytics Engineer II role. Highlight your experience with SQL, dbt, and any relevant data modelling skills. We want to see how your background aligns with what we’re looking for!
Showcase Your Projects:If you've worked on any cool data projects or built analytical models, don’t hold back! Share specific examples that demonstrate your skills and how they can benefit our team. We love seeing real-world applications of your expertise.
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language and avoid jargon where possible. We appreciate a well-structured application that gets straight to the point—just like we do in our data models!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, you’ll find all the details you need about the role and our team there!
How to prepare for a job interview at Spotify
✨Know Your SQL Inside Out
Since strong SQL skills are a must for the Analytics Engineer II role, make sure you brush up on your SQL knowledge. Practice writing complex queries and understand how to optimise them for performance. Being able to demonstrate your SQL prowess during the interview will definitely impress the hiring team.
✨Familiarise Yourself with dbt and BigQuery
As you'll be building analytical data models using dbt in BigQuery, it's crucial to have hands-on experience with these tools. Prepare by working on sample projects or case studies that showcase your ability to create and maintain data models. This will help you speak confidently about your experience during the interview.
✨Understand the Importance of Data Quality
Data quality is key in this role, so be ready to discuss how you've ensured data reliability and testability in your previous work. Think of specific examples where you improved data quality or resolved issues in data pipelines. This will show that you care about delivering high-quality insights.
✨Communicate Clearly with Technical and Non-Technical Partners
You'll need to collaborate with various teams, so practice explaining complex technical concepts in simple terms. Prepare to share examples of how you've successfully communicated with both technical and non-technical stakeholders in the past. This will highlight your ability to bridge the gap between different teams.
