Airbnb Business Analyst Interview Guide – Process, Questions & Tips

As an aspiring Airbnb business analyst, you are stepping into one of the most dynamic and impactful roles in tech today. With Q1 2025 revenue hitting $2.3 billion and over 491 million bookings in 2024, Airbnb’s global growth shows no signs of slowing. International expansion, rising RevPAR, and AI-driven tools are transforming how Airbnb makes decisions. Analysts like you are now essential in turning complex data into strategic insights that influence product launches, global operations, and user experience. The interview process has evolved to match this need, with deeper technical assessments and a sharper focus on business impact. If you’re aiming to join Airbnb’s next chapter, this Airbnb business analyst interview guide will help you get there.

Role Overview & Culture

At Airbnb, the Business Analyst role is designed for someone like you—analytically sharp, cross-functional by nature, and driven to make a real impact. You’ll work across teams to turn raw data into strategies that improve operations, shape product features, and elevate both guest and host experiences. As you build dashboards using Airbnb business analytics tool sets like Tableau and SQL, you’ll also help implement scalable solutions in collaboration with engineering and product teams. While this isn’t a product analyst job description, it shares similarities in its blend of data fluency and user-first thinking. Airbnb’s culture values creativity, inclusion, and global perspective—so your voice will matter. Your skills won’t just support the business. They’ll help shape where it goes next.

Why This Role at Airbnb?

If you’re aiming for a role that blends high-impact work with personal reward, the Airbnb business analyst position is one of the best opportunities in tech right now. You’ll earn a top-tier compensation package—averaging $228,000 annually in the U.S.—while building equity through RSUs and unlocking bonuses for exceptional performance. You’ll also have full access to Airbnb’s global flexibility model, allowing you to live and work anywhere regulations allow. As you dive into advanced tooling like SQL, Python, and Tableau, you’ll directly shape strategy, inform product development, and collaborate with some of the best minds in tech. This role doesn’t just grow your skills and resume. It gives you autonomy, mobility, and real influence. Few roles reward you like the Airbnb business analyst role does.

In the Airbnb business analyst interview process, candidates typically face four-five stages that rigorously assess both your technical expertise and your fit with Airbnb’s mission. Each stage is crafted to evaluate your analytical skills, business acumen, and ability to thrive in a collaborative, high-impact environment. The process usually involves:

• Application Submission
• Recruiter Screen
• SQL and Case Study Screen
• On-Site “Insights Loop”
• Feedback & Hiring Committee

Application Submission

Your journey begins with a focused application that highlights your technical skills, measurable impact, and alignment with Airbnb’s mission. Expect your résumé to be screened for proficiency in SQL, Python, and data visualization, as well as experience in business analytics and statistical modeling. You’ll need to articulate a compelling “Why Airbnb?” pitch, demonstrating your understanding of the company’s global platform and your passion for data-driven decision-making. This is your chance to stand out by quantifying your achievements and showing how your background uniquely positions you to drive value at Airbnb.

Recruiter Screen

If your application impresses, you’ll move to a 30–45 minute recruiter screen. This conversation is friendly but purposeful, focusing on your motivation, technical basics, and cultural fit. You’ll discuss your background, why you’re excited about Airbnb, and how your experience aligns with the business analyst role. Recruiters are looking for clear communication, curiosity, and a genuine connection to Airbnb’s mission. Be ready to answer questions about your past projects, compensation expectations, and what you hope to achieve in this role. This is also your opportunity to ask about the team, company culture, and growth opportunities.

SQL & Case Study Screen

Next, you’ll tackle a technical screen that combines a 30-minute HackerRank SQL assessment with a short case study or deck critique. You’ll be challenged to write queries, interpret data, and solve real-world business problems—often referencing an Airbnb business analytics tool you’ve used or built. This stage tests your ability to extract insights from complex datasets, apply statistical reasoning, and communicate your findings clearly. Expect to demonstrate proficiency in SQL, Python, and data visualization, as well as your approach to A/B testing and business metric analysis. Your performance here is crucial, as it directly reflects your readiness to drive impact at scale.

On-Site “Insights Loop”

If you advance, you’ll experience the on-site “Insights Loop,” a series of four in-depth interviews. You’ll dive into a SQL deep-dive, tackle a forecasting exercise, deliver a stakeholder presentation, and participate in a core-values behavioral interview. Each round is designed to simulate real challenges you’ll face as an Airbnb business analyst, from building data models to presenting actionable recommendations to cross-functional teams. You’ll be evaluated on your technical rigor, business sense, and ability to communicate complex ideas to both technical and non-technical audiences. This is your moment to shine as a data storyteller and strategic partner.

Feedback & Hiring Committee

After your interviews, Airbnb’s process moves swiftly. Interviewers submit detailed feedback within 24 hours, and a hiring committee reviews your performance across all rounds. This committee calibrates for role level (L4 vs. L5) and ensures a fair, unbiased decision. You’ll be assessed not just on technical merit, but also on your alignment with Airbnb’s values and your potential to contribute to the company’s growth. If successful, you’ll receive a competitive offer and join a team where your insights will shape the future of travel and hospitality.

To succeed in the Airbnb interview process, you’ll need to prepare for a diverse mix of questions that test both your technical abilities and your ability to think strategically across business domains.

SQL / Technical Questions

Expect to showcase your Airbnb advanced analytics chops with SQL questions that assess your ability to manipulate complex datasets, detect patterns, and deliver actionable insights for business decisions:

1. Find the total salary of slacking employees

To solve this, use an INNER JOIN to combine the employees and projects tables, filtering for employees who have at least one project assigned but no completed projects (End_dt IS NULL). Group by employee ID and use HAVING COUNT(p.End_dt) = 0 to identify slacking employees. Finally, sum their salaries using a subquery.

2. Write a query to get the average commute time for each commuter in New York

To solve this, use two subqueries: one to calculate the average commute time for each commuter in New York grouped by commuter_id, and another to calculate the overall average commute time across all commuters in New York. Use the TIMESTAMPDIFF function to calculate the duration of each ride in minutes, and then join the results to display both averages in the output.

3. Write a query to retrieve all user IDs whose transactions have exactly a 10-second gap

To solve this, use the LAG() and LEAD() window functions to calculate the time difference between consecutive transactions. Filter the results to include only those transactions with a 10-second gap and return the distinct user IDs in ascending order.

4. Find the average number of accepted friend requests for each age group

To solve this, use a RIGHT JOIN between the requests_accepted and age_groups tables to associate accepted friend requests with age groups. Calculate the average acceptance by dividing the count of accepted requests by the count of unique users in each age group, grouping by age_group, and ordering the results in descending order.

5. Cumulative Sales Since Last Restocking

To calculate cumulative sales since the last restocking, first identify the latest restocking date for each product using the MAX() function grouped by product_id. Then, use a window function SUM(...) OVER() to compute the running total of sales for each product after its last restocking date. Join the sales, products, and the derived table of last restocking dates, filtering sales that occurred after the last restocking date.

Case Study & Forecasting Questions

These questions test how you translate data into forward-looking strategy, often asking you to forecast metrics or build models that align with Airbnb, Inc. forecast and analysis efforts:

6. How would you build a dynamic pricing system for Airbnb based on demand and availability?

To build a dynamic pricing system, gather data on demand, availability, seasonality, and external factors like local events. Use machine learning models, such as regression or reinforcement learning, to predict optimal prices. Consider factors like user behavior, competitor pricing, and elasticity of demand while ensuring the system adapts to real-time changes.

7. How would you forecast revenue for the next year?

To forecast revenue for the next year, analyze historical revenue data for Facebook’s various revenue streams, considering attributes like seasonality and trends. Depending on the behavior of each stream, use models such as classical time series forecasting, ARMA, or ARIMA to predict future revenue, and sum forecasts for all streams to estimate total revenue.

8. Given a task to predict hotel occupancy rates, how would you design the model?

To design the model, start by collecting relevant data such as historical occupancy rates, booking trends, seasonality, holidays, and external factors like local events or weather. Use this data to train a machine learning model, such as regression or time-series forecasting. Evaluate the model’s performance using metrics like Mean Absolute Error (MAE) or Root Mean Square Error (RMSE) to ensure accuracy.

9. Call Center Resource Management

To address this problem, a predictive model such as a time-series forecasting model or a regression model can be used to predict call volumes and allocate agents accordingly. Metrics to evaluate the model include accuracy in predicting call volumes, agent utilization rates, and customer wait times. Over-allocation ensures better customer satisfaction but may increase costs, while under-allocation risks longer wait times and lower customer satisfaction. Balancing these trade-offs is key to determining the optimal allocation strategy.

10. How would you build a function to return a list of daily forecasted revenue starting from Day 1 to the end of the quarter (Day N)?

To solve this, calculate the daily growth rate by dividing the difference between the total revenue target and Day 1 revenue by the number of days minus one. Then, iteratively compute the revenue for each day by adding the daily growth rate to the previous day’s revenue, storing the results in a list.

Dashboard & Tooling Questions

You’ll be asked to critique and design visualizations, evaluate metrics, and improve user understanding using tools like Minerva and Superset, often within the context of an Airbnb analytics dashboard or other Airbnb analysis software:

11. How would you visualize data with long tail text to effectively convey its characteristics and help extract actionable insights?

To visualize long-tail text data, start with frequency distribution plots like log-log scale histograms or Zipf plots to highlight keyword occurrences. Use semantic analysis techniques such as word clouds or clustering methods like t-SNE to uncover patterns. Integrate these insights with conversion metrics and temporal trends into dashboards for actionable business decisions.

12. Design a dashboard that provides personalized insights, sales forecasts, and inventory recommendations for shop owners

To design a merchant dashboard, prioritize metrics like sales trends (e.g., week-over-week revenue changes), inventory insights (e.g., days remaining for stock), and customer behavior (e.g., repeat purchase rates). Use adaptive visualizations tailored to merchant types, such as cohort charts for customer segmentation or smart banners highlighting actionable insights. Ensure scalability by leveraging metadata to personalize layouts and validate utility through merchant interaction tracking.

13. Interpreting Fraud Detection Trends

To interpret fraud detection graphs, focus on identifying anomalies, spikes, or patterns in fraudulent activities over time. Key insights include understanding the frequency, timing, and types of fraud, which can help refine detection algorithms and implement preventive measures. Use these insights to improve fraud detection processes by enhancing model accuracy, updating rules, and deploying real-time monitoring systems.

14. Critique a Minerva dashboard that shows booking trends by city. What would you improve?

Start by evaluating clarity and usefulness. Are city-level metrics aggregated at appropriate levels? Look for over-cluttered visualizations, inconsistent color schemes, or missing comparison baselines like year-over-year trends. Suggest adding filters for dates, guest demographics, and property types. Finally, recommend aligning KPI definitions across the airbnb analytics dashboard so regional teams interpret the data consistently.

Behavioral & Values Questions

Here, interviewers are looking to understand how you collaborate, communicate, and live out Airbnb’s values—especially in moments of ambiguity, cross-functional tension, or high-stakes decision-making:

15. How would you convey insights and the methods you use to a non-technical audience?

At Airbnb, business analysts frequently collaborate with design, operations, and regional teams that may not have technical expertise. You should describe how you structure your insights using storytelling, visualizations, and real-world analogies. For example, you might walk through how you used clustering to segment guests by travel behavior, then presented clear visuals and trade-offs to help a marketing team prioritize a campaign. Focus on simplifying without oversimplifying.

16. What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Tailor your answer to qualities that align with Airbnb’s values. For strengths, consider areas like strong SQL proficiency, experience with experimentation platforms, or a track record of turning ambiguous problems into structured analysis. For weaknesses, avoid clichés. Instead, be honest and share how you’ve addressed it. For example, you might say you previously over-indexed on perfection in dashboards but learned that speed-to-insight is more critical in fast-paced teams like Airbnb’s regional operations.

17. Why Do You Want to Work With Us

Go beyond generic admiration. In 2025, Airbnb continues to lead in the intersection of travel, trust, and technology. Reference your alignment with Airbnb’s mission and how you’re excited to contribute to key initiatives, such as sustainable travel growth or optimizing the host onboarding experience. Mention interest in working with global teams, using real-time data to drive product or marketplace strategy, and leveraging Airbnb’s strong analytics infrastructure to make meaningful impact.

18. Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

Airbnb analysts often sit between product, operations, and regional leadership. When there is disagreement or confusion, it is your role to bridge data and narrative. Provide a story where you had to identify why your insight wasn’t resonating, then explain how you reframed the data or involved the stakeholders earlier in the analysis process. Focus on active listening, using visual tools, or scenario modeling to create alignment and improve decision-making.

To excel in the Airbnb business analyst interview, you’ll want to master both technical and strategic preparation. Start by honing your SQL and Tableau skills, as you’ll be expected to analyze complex datasets and visualize insights using an airbnb business analytics tool. Practice writing advanced queries that extract actionable trends from booking, pricing, and review data—these are core to the technical screens and on-site exercises. Airbnb’s interviewers value candidates who can not only manipulate data but also translate findings into business recommendations using clear, compelling dashboards.

Building your own forecasting projects is a powerful way to stand out. Leverage the open-source Airbnb review dataset, which contains millions of real guest reviews and listing details from global cities. Use this dataset to practice exploratory data analysis, sentiment mining, and predictive modeling—skills that directly mirror the challenges you’ll face at Airbnb. For example, you might forecast review scores or booking trends using Python and Tableau, then present your findings as you would to a cross-functional team. Practice with our AI Interviewer to gain more clarity on the approach to the ideal answers.

Equally important is your ability to tell stories with data and align with Airbnb’s core values. Use the STAR method (Situation, Task, Action, Result) to structure your behavioral answers, and be ready to demonstrate how you “Champion the Mission” by connecting your work to Airbnb’s vision of belonging and community impact.

Finally, simulate the interview environment with mock interviews and peer feedback. Benchmark your SQL speed and accuracy on data-driven platforms, and seek out realistic case studies to sharpen your business sense. This holistic, hands-on approach will help you approach the process with confidence and clarity, ready to make a measurable impact from day one.

What Is the Average Salary for an Airbnb Business Analyst?

How Many Airbnb Business Analyst Jobs Open Each Year?

On LinkedIn and similar platforms, Airbnb consistently posts dozens of openings annually for Airbnb business analyst roles, often ranging from mid- to senior-level. These openings reflect the company’s expansion across new markets in Latin America and Asia Pacific as it scales its analytics capabilities.

Which Analytics Tools Does Airbnb Use Most?

Airbnb relies heavily on internal platforms like Minerva for centralized metrics and on Superset and other BI systems for exploration and reporting. That reflects their emphasis on Airbnb’s advanced analytics, empowering analysts to generate consistent, reliable insights at scale.

If you’re serious about joining one of tech’s most impactful companies, now is the perfect time to start preparing for the Airbnb business analyst interview. The role is competitive, but your preparation doesn’t have to be overwhelming. Explore our Airbnb Analyst Learning Path to build the core skills in SQL, forecasting, and dashboarding. Draw inspiration from this Success Story and learn how others turned data fluency into a full-time offer. Finally, dig into our full Airbnb SQL Business Analyst Questions Bank to sharpen your instincts on what really gets asked. A well-planned approach turns the Airbnb business analyst interview into a storytelling opportunity.