To succeed in the Spotify interview process, you must prepare across multiple dimensions. The hiring team looks for well-rounded professionals who can write clean queries, structure complex business problems, and present their findings with confidence.

Technical Competence – You must be highly proficient in SQL and comfortable discussing relational database structures. Your interviewers will evaluate how cleanly and efficiently you write queries, as well as your ability to identify data anomalies and structure data models.

Structured Problem-Solving – When presented with an ambiguous business scenario, you should not jump straight to an answer. Interviewers want to see you break down the problem into logical components, establish clear hypotheses, and explain how you would leverage data to test those hypotheses.

Business Acumen & Modeling – You need to understand how Spotify makes money, including the mechanics of the Premium subscription model and the ad-supported tier. Be prepared to discuss financial modeling, pricing strategies, and key SaaS metrics like Customer Acquisition Cost (CAC), Lifetime Value (LTV), and churn.

Stakeholder Communication – A great Business Analyst is a storyteller. You must demonstrate the ability to translate complex technical findings into simple, actionable insights for non-technical stakeholders, directors, and cross-functional partners.

The interview process for a Business Analyst at Spotify is structured to evaluate both your technical execution and your collaborative fit. While the process is rigorous, candidates frequently report that the team members they interact with are highly collaborative and eager to understand how you think.

The recruitment pipeline typically begins with an initial HR screening to assess your background and interest. This is followed by a hiring manager interview and a dedicated technical assessment—often focusing heavily on SQL. Depending on the team and location, you may also be asked to complete a take-home case study where you will analyze a dataset, build a financial or operational model, and compile a presentation deck. The final round consists of back-to-back interviews with cross-functional stakeholders, team members, and directors.

The visual timeline above outlines the typical progression of the Spotify interview stages. Candidates should use this timeline to pace their preparation, ensuring they focus on SQL and case study frameworks early on before shifting their attention to high-level stakeholder communication and behavioral alignment for the final rounds. While the process can span several weeks, maintaining momentum and staying prepared at each stage is key to success.

SQL & Data Wrangling

The technical screen is designed to ensure you can independently extract and manipulate the data required for your analyses. You will be evaluated on your speed, accuracy, and understanding of relational database design.

Be ready to go over:

• Data Aggregation and Grouping – Using aggregate functions combined with complex grouping to analyze user patterns.
• Window Functions – Utilizing ROW_NUMBER(), RANK(), and LAG/LEAD to analyze sequential user behaviors over time.
• Query Performance – Understanding how index usage, subqueries, and table joins impact execution speed on large datasets.
• Advanced concepts (less common) – Writing recursive queries, managing temporary tables, and understanding database normalization principles.

Example questions or scenarios:

• "Write a query to calculate the month-over-month retention rate of Premium users."
• "Identify users who listened to at least three different genres in their first week of signing up."
• "How would you handle duplicate stream entries in a raw event log table?"

Business Case Analysis & Presentation

If your loop includes a take-home case study, you will receive a dataset and an open-ended business question. This stage evaluates your ability to build structured models, draw strategic conclusions, and present your findings clearly to leadership.

Be ready to go over:

• Financial & Operational Modeling – Building forecasting models using Excel or similar tools to project revenue, costs, or user growth.
• Data Visualization – Designing clear, uncluttered charts that highlight key trends and support your recommendations.
• Executive Presentation – Structuring a slide deck that starts with a clear executive summary followed by data-backed supporting evidence.
• Advanced concepts (less common) – Incorporating sensitivity analysis or Monte Carlo simulations to account for risk and uncertainty in your projections.

Example questions or scenarios:

• "Analyze this dataset of user listening behavior and recommend whether we should invest more in podcast licensing or music curation."
• "Build a financial model projecting the revenue impact of a 10% price increase for Premium Duo in European markets."
• "Present a 5-slide deck outlining a strategic plan to reduce churn among ad-supported listeners."

Behavioral & Values Alignment

Spotify places a high premium on its company culture, which emphasizes innovation, collaboration, sincerity, passion, and playfulness. Behavioral interviews focus on how you work with others and navigate professional challenges.

Be ready to go over:

• Handling Conflict – How you resolve disagreements with stakeholders or team members regarding data interpretation or priorities.
• Navigating Ambiguity – Demonstrating how you make progress on analytical projects when data is incomplete or objectives are poorly defined.
• Growth Mindset – Sharing examples of how you have learned from failures or sought out feedback to improve your work.

Example questions or scenarios:

• "Tell me about a time you delivered an analysis that turned out to have an error. How did you handle it with your stakeholders?"
• "Describe a situation where you had to prioritize multiple urgent data requests from different departments."
• "Give an example of a time you used data to challenge a deeply held assumption within your team."

As a Business Analyst at Spotify, your day-to-day work will be highly dynamic and collaborative. You will not work in a silo; instead, you will act as the data-driven anchor for your team, translating raw metrics into business progress.

Your primary focus will be on extracting, cleaning, and analyzing data to evaluate product performance and business health. This involves writing complex SQL queries to pull data from internal data warehouses, performing exploratory data analysis, and building automated dashboards to monitor key performance indicators (KPIs). You will closely monitor metrics such as Daily Active Users (DAU), Monthly Active Users (MAU), average revenue per user (ARPU), and churn rates.

In addition to monitoring health metrics, you will partner with product managers and engineers to support product experimentation. You will help design A/B tests, define success metrics, and analyze experiment results to determine whether new features should be rolled out globally. Your insights will directly influence product roadmaps and strategic business cases.

Finally, you will spend a significant portion of your time communicating with stakeholders. You will package your analyses into clear, structured documents and presentations for business leaders, helping them understand the "why" behind the numbers. Whether presenting to product directors or finance managers, you will serve as a trusted advisor, ensuring that strategic decisions are grounded in robust data.