What is a Data Engineer?

At Airbnb, the Data Engineer role is central to the company’s mission of creating a world where anyone can belong anywhere. Data is not just a byproduct of the platform; it is the engine that drives decision-making, product innovation, and the personalized experiences of millions of hosts and guests. As a Data Engineer here, you are not simply moving data from point A to point B. You are architecting the foundation that enables the entire organization—from Product Managers to Data Scientists—to understand user behavior, optimize search rankings, and ensure trust and safety across the platform.

You will work on high-scale challenges that involve processing petabytes of data. Airbnb is historically known for its significant contributions to the open-source data community (most notably creating Apache Airflow and Apache Superset). Consequently, the bar for engineering excellence is high. You will build robust, scalable data pipelines, design efficient data models, and maintain the "source of truth" datasets (often referred to as "Minerva" metrics within the company) that power critical business dashboards and machine learning models.

This role requires a blend of strong software engineering principles and deep data intuition. You will sit at the intersection of infrastructure and analytics, ensuring that data is reliable, timely, and accessible. Whether you are optimizing batch processing jobs or designing real-time streaming architectures, your work directly impacts how Airbnb scales its global marketplace.

Common Interview Questions

The following questions are representative of what candidates have encountered. They are categorized to help you structure your practice sessions.

Data Modeling & System Design

This category tests your ability to think at a high level about data architecture.

• "Design a data warehouse solution for a ride-sharing app (similar to Uber/Lyft). What tables do you need?"
• "How would you model the data for Airbnb's search ranking system? How do you handle historical changes in listing attributes?"
• "We have a table with 100 billion rows that is queried frequently by date and city. How do you optimize it?"
• "Explain the difference between a Star Schema and a Snowflake Schema. When would you use one over the other?"

SQL & Analytical Querying

These questions test your raw ability to extract insights from data.

• "Write a query to find the top 3 hosts in each city based on the number of positive reviews."
• "Given a table of bookings and a table of cancellations, calculate the cancellation rate for each month."
• "Identify users who booked the same listing more than once within a 30-day window."
• "How would you de-duplicate a table that has no primary key?"

Coding & Algorithms

These questions ensure you can write functional scripts to process data.

• "Given a list of strings, group them by anagrams."
• "Write a program to read a CSV file of transactions and sum the total revenue per category."
• "Implement a function to flatten a nested JSON object."
• "Find the first non-repeating character in a stream of characters."

These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Getting Ready for Your Interviews

Preparing for an engineering role at Airbnb requires a strategic approach. Unlike general software engineering roles that focus heavily on complex algorithms, the Data Engineer loop balances coding proficiency with practical data manipulation and system design. You need to demonstrate that you can build systems that are not only functional but also maintainable and scalable.

Your interviewers will evaluate you based on the following key criteria:

Technical Proficiency – You must demonstrate fluency in SQL and a procedural language (typically Python or Java). Interviewers look for clean, production-quality code. It is not enough to get the right answer; your code should handle edge cases, error logging, and be optimized for performance.

Data Modeling & Architecture – This is a critical evaluation area. You will be assessed on your ability to design schemas (Star vs. Snowflake), optimize for read/write patterns, and handle data partitioning. You must understand how to structure data to answer specific business questions efficiently.

Core Values Alignment – Airbnb places immense weight on culture. You will be evaluated on how well you embody values like "Be a Host" and "Champion the Mission." This is not a "soft" check; it is a rigorous part of the process where you must demonstrate empathy, collaboration, and a positive approach to ambiguity.

Interview Process Overview

The interview process at Airbnb is structured to be thorough and transparent. It typically begins with a recruiter screening to discuss your background and interest in the role. If you pass this stage, you will move to a technical phone screen. This screen usually involves a coding challenge—often focused on SQL and light algorithmic scripting—conducted via a shared code editor. The goal here is to verify your fundamental technical skills before moving to the comprehensive onsite loop.

The onsite stage (currently virtual) is an immersive experience consisting of multiple rounds. You should expect a mix of "General Coding" rounds (algorithmic problems), "Data Coding" rounds (practical data manipulation), and a dedicated "Experience" or "Architecture" round where you discuss past projects and system design. Uniquely, Airbnb includes a specific "Core Values" interview, which is separate from technical assessments and carries significant weight in the final hiring decision.

The process is rigorous but designed to be collaborative. Interviewers want to see how you think and how you collaborate on problems. The pace is generally steady, and you can expect the entire loop—from application to offer decision—to take several weeks depending on scheduling.

This timeline illustrates the typical flow from your initial application to the final decision. Use this visual to plan your study schedule; ensure you allocate time specifically for the "Values" interview preparation, as it appears late in the process but is a common stumbling block for otherwise technically strong candidates.

Deep Dive into Evaluation Areas

Based on candidate reports and internal standards, the Data Engineer interview focuses on three to four primary pillars. You must demonstrate competence in all of them to receive an offer.

Data Modeling and Schema Design

This is often the defining round for Data Engineers. You will be given a vague business scenario (e.g., "Design the data tables for a hotel booking system") and asked to architect the solution.

Be ready to go over:

• Dimensional Modeling – Deep understanding of Facts, Dimensions, and Star Schemas.
• Partitioning & Bucketing – Strategies for optimizing storage and query performance on large datasets (e.g., partitioning by date or region).
• Schema Evolution – How to handle changes in data structure over time without breaking downstream consumers.
• Scaling – How your design holds up when data volume increases by 10x or 100x.

Example questions or scenarios:

• "Design a database schema to track Airbnb experiences and bookings. How would you handle price changes over time?"
• "How would you partition a table with billions of rows to ensure fast retrieval for regional analysis?"
• "Critique this existing schema. Where are the bottlenecks?"

SQL and Data Manipulation

Expect to write complex SQL queries from scratch. These questions go beyond simple SELECT * statements. You will need to solve analytical problems using SQL.

Be ready to go over:

• Window Functions – Proficient use of RANK(), LEAD(), LAG(), and moving averages.
• Complex Joins – Handling self-joins, cross-joins, and optimizing join logic.
• Aggregation & Filtering – Advanced grouping and filtering techniques (HAVING, CASE WHEN).

Example questions or scenarios:

• "Given a table of user logins, write a query to find users who have logged in on 3 consecutive days."
• "Calculate the week-over-week growth rate of bookings per city."

Algorithmic Coding (The "Laptop" or "General Coding" Round)

While less intense than a backend SWE interview, you still need to write clean code in Python or Java. The focus is often on data structures and practical manipulation rather than dynamic programming or graph theory.

Be ready to go over:

• Data Structures – Arrays, HashMaps (Dictionaries), Sets, and Lists.
• String Manipulation – Parsing logs, cleaning messy text data.
• File I/O – Reading a CSV or JSON file and processing it line-by-line.

Example questions or scenarios:

• "Write a function to parse a messy log file and extract error counts per module."
• "Find the median of a stream of numbers."
• "Implement a simple rate limiter."