At a Glance
- Tasks: Build and maintain analytical models and data products that drive Spotify's growth.
- Company: Join Spotify, a leading platform in music streaming and technology innovation.
- Benefits: Flexible work environment, competitive salary, and opportunities for professional growth.
- Other info: Collaborative team culture with a focus on data quality and innovation.
- Why this job: Make an impact on how Spotify scales and improves developer productivity.
- Qualifications: 2+ years in analytics engineering, strong SQL skills, and experience with dbt.
The predicted salary is between 50000 - 60000 £ per year.
Requirements
- 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 prioritise and deliver in a fast-moving environment.
- You have experience with platform or developer productivity data, experimentation, or ML/AI metrics.
What the job involves
- 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.
- 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.
- 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.
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.
Analytics Engineer employer: Spotify
At Spotify, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. As an Analytics Engineer II, you'll have the opportunity to work with cutting-edge technologies in a dynamic environment that values data quality and reliability. With flexible working arrangements and a commitment to employee growth, you'll be empowered to make meaningful contributions while advancing your career in a supportive team atmosphere.
StudySmarter Expert Advice🤫
We think this is how you could land Analytics Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your analytics projects, especially those involving SQL, dbt, or data visualisation tools like Looker or Tableau. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on common analytics engineering questions. Be ready to discuss your experience with data modelling, pipeline reliability, and how you ensure data quality. Practice makes perfect!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to get noticed by our hiring team. Plus, it shows you’re genuinely interested in joining us at StudySmarter and contributing to our mission.
We think you need these skills to ace Analytics Engineer
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your 2+ years of experience in analytics engineering or data engineering. We want to see your strong SQL skills and any experience with dbt or similar frameworks. Don’t be shy about showcasing your technical prowess!
Tailor Your Application:When applying, tailor your application to reflect the job description. Mention your familiarity with cloud data warehouses like BigQuery or Snowflake, and how you've used workflow orchestration tools. This helps us see how you fit into our team!
Communicate Clearly:Since you'll be working with both technical and non-technical partners, make sure your application reflects your ability to communicate clearly. Use straightforward language and examples that demonstrate your collaborative spirit.
Apply Through Our Website:We encourage you to apply through our website for a smoother process. It’s the best way for us to receive your application and get you on our radar. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Spotify
✨Show Off Your SQL Skills
Make sure to brush up on your SQL skills before the interview. Be ready to discuss your experience with data modelling and how you've used SQL in past projects. You might even be asked to solve a problem on the spot, so practice some common SQL queries to feel confident.
✨Familiarise Yourself with dbt and Cloud Warehouses
Since the role involves working with dbt and cloud data warehouses like BigQuery or Snowflake, it’s crucial to understand these tools inside out. Prepare examples of how you’ve used them in previous roles, focusing on how they helped improve data quality and reliability.
✨Communicate Clearly
You’ll need to communicate with both technical and non-technical partners, so practice explaining complex concepts in simple terms. Think of examples where you successfully collaborated with different teams and how you ensured everyone was on the same page.
✨Demonstrate Your Problem-Solving Skills
Be prepared to discuss how you approach troubleshooting and improving data pipelines. Share specific instances where you identified issues and implemented solutions, especially regarding data quality and performance. This will show that you care about delivering reliable results.
