Airbnb Data Engineer Interview Questions + Guide 2024

In the ever-evolving world of travel, Airbnb has become synonymous with unique experiences and connecting people through hospitality, with over 448 million bookings worldwide. But the platform’s success hinges on a team of unseen heroes: data engineers.

These tech experts are the masterminds behind the scenes, designing, building, and maintaining the complex data systems that power Airbnb. From optimizing search results to ensuring smooth transactions, data engineers play a critical role in keeping Airbnb running like a well-oiled machine.

This article will explore the essential technical skills needed to excel and a breakdown of interview questions you might encounter. Get ready to learn everything you need to know and launch your dream career as an Airbnb data engineer.

While the exact details of Airbnb’s interview process can vary depending on the specific role and team, here’s a general overview of what you can expect as a data engineer candidate:

Initial Screening

An initial phone conversation with an HR representative kicks off your interview journey with Airbnb. This is also your chance to introduce yourself, showcase your enthusiasm for the role, and ensure your background aligns with Airbnb’s requirements. Be prepared to discuss your relevant experience, technical skills, and why you’re interested in Airbnb.

Technical Screens

This stage typically involves a combination of online coding assessments and technical phone interviews. The coding assessments involve solving problems related to data structures, algorithms, and SQL queries. Technical phone interviews are likely to delve deeper into your technical expertise.

Be prepared to write complex queries to analyze data, design tables, and optimize for efficiency. You’ll also likely be tested on your proficiency in Python, a core language for data engineering at Airbnb. Questions might involve data manipulation, building data pipelines, or working with APIs.

Airbnb puts a strong emphasis on data modeling skills. You might be presented with a business problem and asked to design the appropriate data schemas and tables to solve it.

On-site Interviews

If you progress past the technical screens, you’ll be invited for on-site interviews. This will likely involve a series of interviews with various team members, including data engineers, engineering managers, and potentially even product managers or data scientists, depending on the role. Here’s a breakdown of what types of questions you might encounter:

• System design: You’ll be presented with a real-world Airbnb data engineering problem and expected to design a scalable and efficient system to address it. This could involve discussing trade-offs, fault tolerance, and how your design would integrate with existing infrastructure.
• Behavioral and cultural fit: These interviews assess your soft skills, communication style, and how you would fit into Airbnb’s collaborative work environment. Be prepared to discuss your problem-solving approach, teamwork experience, and what motivates you.
• Data engineering questions: These questions go beyond general technical skills and delve into your experience with specific data engineering tools and technologies relevant to Airbnb’s data stack (e.g., BigQuery, Apache Airflow, Spark). You might be asked about your experience with data warehousing, pipelines, and quality practices or how you tackle big data challenges.

Throughout the interview process, showcase your passion for data and ability to translate business problems into technical solutions. Be prepared to discuss your experience with big data concepts like distributed computing and data lakes. By understanding the typical interview structure and focusing on the areas discussed, you can significantly increase your chances of success in landing a data engineer role at Airbnb.

Let’s dive deep into the specific questions you might encounter during your Airbnb data engineer interview. We’ll explore technical areas like SQL mastery, Python proficiency, and data modeling. You’ll also discover how to tackle behavioral questions that assess your problem-solving approach, teamwork abilities, and overall cultural fit within Airbnb’s innovative environment.

1. What would your current manager say about you? What constructive criticisms might he give?
2. How comfortable are you presenting your insights?
3. Describe a data project you worked on. What were some of the challenges you faced?
4. Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?
5. What are you looking for in your next job?
6. Let’s say you have analytics data stored in a data lake. An analyst tells you they need hourly, daily, and weekly active user data for a dashboard that refreshes every hour. How would you build this data pipeline?
7. Let’s say that you worked as a machine learning engineer at Airbnb. You’re required to build a new dynamic pricing algorithm based on the demand and availability of listings. How would you build a dynamic pricing system? What considerations would have to be made?
8. Let’s say you’re tasked with building the YouTube video recommendation algorithm. How would you design the recommendation system? What are important factors to keep in mind when building the recommendation algorithm?
9. Why is it standard practice to explicitly put foreign key constraints on related tables instead of creating a normal BIGINT field? When considering foreign key constraints, when should you consider a cascade delete or a set null?
10. You are tasked with keeping track of a customer’s address in your database. However, customer’s addresses change whenever they move. We want to keep track of their moving history as well as the person who moves in afterward. How would you create or modify a schema to keep track of these address changes?
11. Explain the difference between a common table expression (CTE) and a subquery. When would you use each?
12. Given a large dataset of user booking behavior, write a Python script using pandas to identify user segments with similar booking patterns.
13. Write a Python function that utilizes an API to retrieve real-time pricing data for a specific listing and compare it to historical data to identify potential pricing trends.
14. Briefly explain the difference between supervised and unsupervised learning algorithms. Can you give an example of how each might be used in the context of Airbnb?
15. Imagine you’re building a recommendation system for suggesting listings to users. What factors might you consider as features for your machine learning model?
16. How would you define and measure the key metrics for a successful listing on Airbnb (e.g., booking conversion rate and occupancy rate)?
17. Explain the concept of time complexity and how it applies to different sorting algorithms (e.g., bubble sort vs quicksort).
18. Explain the difference between descriptive and predictive analytics. How can data engineers contribute to both types of analytics at Airbnb?
19. Describe your approach to analyzing a large dataset to identify trends or patterns relevant to Airbnb’s business goals.
20. Given a large dataset of user locations, how would you efficiently identify nearby listings for a specific user search query? (e.g., k-nearest neighbors)

Cracking a data engineer interview at Airbnb requires a multi-faceted approach. You’ll need to showcase not only your technical prowess but also your ability to think strategically, solve problems creatively, and collaborate effectively. Here’s a roadmap to guide you through the preparation process, focusing on both technical and behavioral aspects:

Deep Dive into Airbnb

Research Airbnb’s data stack and the technologies they use. Familiarize yourself with the data engineering challenges they might face. Tailor your examples and discussions to showcase your understanding of their specific environment.

Master SQL Querying and Manipulation

Airbnb relies heavily on SQL for data retrieval and manipulation. Brush up on writing complex queries that can filter, aggregate, and join data efficiently. Be prepared to demonstrate proficiency in concepts like window functions, common table expressions (CTEs), and subqueries.

Revise Your Python Skills

To prepare for questions using Airbnb’s primary data engineering language, hone your Python skills. Practice Python questions about data manipulation libraries like pandas, data analysis tools like NumPy, and frameworks like PySpark for distributed processing. Be ready to write clean, efficient code, demonstrating your understanding of object-oriented programming principles and best practices.

Refine Machine Learning and Data Modeling Skills

Data modeling and machine learning are core requirements for Airbnb data engineers. Grasp the principles of dimensional modeling and star/snowflake schema design. Practice translating business requirements into logical and physical data models that optimize performance and scalability.

Learn About Big Data

Familiarity with big data concepts like distributed computing frameworks like Apache Spark and data lakes like AWS S3 is a plus. Understanding how to handle large datasets efficiently and exploring tools like Apache Airflow for data pipeline orchestration will set you apart.

Sharpen Your Problem-Solving Skills

Be prepared to showcase your problem-solving approach during technical interviews. Present a structured thought process, explain your assumptions clearly, and walk the interviewer through your thought process. Practice solving data engineering challenges like designing data pipelines, optimizing data quality, or handling data inconsistencies.

Hone Your Communication Skills

Strong communication is vital for collaborating effectively with various teams. Practice articulating technical concepts clearly and concisely, both verbally and in writing. Be prepared to explain your thought process and code effectively during interviews. Consider participating in mock interviews to gain valuable feedback on your communication style, problem-solving approach, and overall interview presence.

What is the average salary for a data engineer at Airbnb?

Airbnb compensates its data engineers pretty well, with an average base salary of around $185,000 and a total compensation of over $260,000. With experience, you can maximize the base salary to $220,000 and earn a total compensation of around $419,000. Find more about data engineer salaries in our detailed salary guide.

What other companies are hiring data engineers besides Airbnb?

While Airbnb is a great option, many companies are looking for talented data engineers. Tech giants like Google and Amazon offer competitive packages and cutting-edge projects. Finance and retail giants like JP Morgan Chase and Netflix also rely heavily on data engineers.

Does Interview Query have job postings for the Airbnb data engineer role?

Yes, depending on the availability of the position, we frequently update our job board to directly connect you to the companies. Feel free to solve the interview questions and apply through our portal.

By incorporating the in-depth technical insights and interview prep strategies outlined in this guide, you’ll be well on your way to acing the Airbnb data engineer interview process.

If you’re interested in other data-based roles offered by Airbnb, explore their business analyst, data analyst, and data scientist roles in the main Airbnb Interview Guide.

Remember, showcase your passion for data, problem-solving prowess, and collaborative spirit—these are the key ingredients to landing your dream role of contributing to Airbnb’s data-driven success story!