OfferUp Software Engineer Interview Questions + Guide in 2025

OfferUp is the largest mobile marketplace for local buyers and sellers in the U.S., revolutionizing the way people connect over commerce in their communities.

As a Software Engineer at OfferUp, you will be an integral part of a dynamic team responsible for developing innovative solutions that enhance user experiences across the platform. Key responsibilities include designing and implementing scalable software solutions, collaborating with cross-functional teams to address complex business problems, and contributing to system architecture and design. You will be expected to demonstrate proficiency in algorithms, data structures, and system design, as well as a strong understanding of both relational and NoSQL databases. This role requires a solid foundation in coding, ideally using languages such as Java, JavaScript, and TypeScript, while also being adaptable to the evolving landscape of technology.

The ideal candidate embraces a fast-paced, ownership-driven environment and possesses strong problem-solving skills with a focus on practical application. You’ll need to be a team player with a passion for mentoring and developing others, while also contributing your own ideas to the engineering roadmap. Familiarity with cloud infrastructure and experience in advertising technologies can be a plus, as OfferUp seeks to enhance its advertising platform for better user engagement.

This guide will equip you with the necessary insights to navigate the interview process effectively, helping you stand out by aligning your skills and values with OfferUp's mission and culture.

The interview process for a Software Engineer at OfferUp is structured to assess both technical skills and cultural fit within the company. It typically consists of several stages, each designed to evaluate different aspects of a candidate's qualifications and compatibility with OfferUp's values.

1. Initial Phone Screen

The process begins with an initial phone screen conducted by a recruiter. This conversation usually lasts about 30 minutes and focuses on understanding your background, skills, and motivations for applying to OfferUp. The recruiter will also assess your cultural fit and provide insights into the company’s work environment and expectations.

2. Technical Phone Screens

Following the initial screen, candidates typically undergo two technical phone interviews. These interviews are designed to evaluate your problem-solving abilities and coding skills. You can expect to tackle algorithmic challenges, often sourced from platforms like LeetCode, with varying levels of difficulty. Interviewers may also ask questions related to data structures and system design, so be prepared to demonstrate your understanding of these concepts.

3. Onsite Interviews

The onsite interview stage usually consists of four technical interviews, which may include coding challenges, system design discussions, and behavioral questions. Each interview lasts approximately 45 minutes, and candidates may also participate in a non-technical lunch interview, which is more about assessing cultural fit than technical skills. The interviewers are typically engineers from the team you would be joining, and they will be interested in your approach to problem-solving and your ability to communicate effectively.

4. Feedback and Offer

After the onsite interviews, candidates can expect prompt feedback from the recruiting team, often within a few business days. If successful, you will receive an offer that includes details about compensation, benefits, and other relevant information.

As you prepare for your interviews, it’s essential to familiarize yourself with the types of questions that may be asked during the process.

In this section, we’ll review the various interview questions that might be asked during a Software Engineer interview at OfferUp. The interview process will likely focus on your technical skills, particularly in algorithms, data structures, and system design, as well as your ability to work collaboratively in a team environment. Be prepared to demonstrate your problem-solving abilities and your understanding of software engineering principles.

Algorithms and Data Structures

1. Can you explain the difference between a stack and a queue?

Understanding fundamental data structures is crucial for any software engineering role.

How to Answer

Discuss the definitions of both data structures, their use cases, and how they operate (LIFO for stacks and FIFO for queues).

Example

“A stack is a data structure that follows the Last In First Out principle, meaning the last element added is the first to be removed. It’s commonly used in scenarios like function call management in programming. A queue, on the other hand, follows the First In First Out principle, where the first element added is the first to be removed, making it ideal for scenarios like task scheduling.”

2. Describe a situation where you optimized an algorithm. What was the original complexity, and what did you achieve?

This question assesses your practical experience with algorithm optimization.

How to Answer

Provide a specific example, detailing the original algorithm's complexity, the changes you made, and the resulting performance improvements.

Example

“I worked on a sorting algorithm that initially had a time complexity of O(n^2). By implementing a quicksort algorithm, I reduced the complexity to O(n log n), which significantly improved the performance of our data processing tasks, especially with larger datasets.”

3. How would you implement a binary search algorithm?

Binary search is a classic algorithm that demonstrates your understanding of searching techniques.

How to Answer

Explain the binary search process and provide a brief overview of its implementation.

Example

“Binary search works on sorted arrays by repeatedly dividing the search interval in half. If the target value is less than the middle element, the search continues in the lower half; otherwise, it continues in the upper half. This results in a time complexity of O(log n).”

4. What is a hash table, and how does it work?

This question tests your knowledge of data structures and their applications.

How to Answer

Discuss the concept of hash tables, including how they store key-value pairs and handle collisions.

Example

“A hash table is a data structure that maps keys to values for efficient data retrieval. It uses a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. Collision resolution techniques, such as chaining or open addressing, are used to handle cases where multiple keys hash to the same index.”

5. Can you explain the concept of recursion and provide an example?

Recursion is a fundamental programming concept that is often tested in interviews.

How to Answer

Define recursion and provide a simple example, such as calculating the factorial of a number.

Example

“Recursion is a method where a function calls itself to solve smaller instances of the same problem. For example, to calculate the factorial of a number n, the function can call itself with n-1 until it reaches the base case of 1.”

System Design

1. How would you design a URL shortening service?

This question evaluates your system design skills and ability to think through complex problems.

How to Answer

Outline the key components of the system, including the database schema, API endpoints, and how you would handle scalability.

Example

“I would design a URL shortening service with a database to store the original URLs and their shortened versions. The API would have endpoints for creating a short URL and redirecting to the original URL. To handle scalability, I would implement caching for frequently accessed URLs and use a load balancer to distribute traffic.”

2. Describe how you would design a messaging system.

This question assesses your ability to design systems that require real-time communication.

How to Answer

Discuss the architecture, including message queues, database choices, and how you would ensure message delivery.

Example

“I would design a messaging system using a publish-subscribe model with a message broker like RabbitMQ. Each message would be stored in a database for persistence, and I would implement acknowledgments to ensure messages are delivered reliably. For real-time communication, I would use WebSockets to push messages to clients.”

3. What considerations would you take into account when designing a large-scale distributed system?

This question tests your understanding of distributed systems and their challenges.

How to Answer

Discuss factors such as data consistency, fault tolerance, and load balancing.

Example

“When designing a large-scale distributed system, I would consider data consistency models, such as eventual consistency versus strong consistency, to ensure data integrity. I would also implement fault tolerance through redundancy and replication, and use load balancing to distribute traffic evenly across servers.”

4. How would you approach designing a database schema for an e-commerce application?

This question evaluates your database design skills and understanding of application requirements.

How to Answer

Outline the key entities, relationships, and normalization principles you would apply.

Example

“I would start by identifying key entities such as Users, Products, Orders, and Reviews. I would establish relationships between these entities, ensuring normalization to reduce redundancy. For instance, a User can have multiple Orders, and each Order can contain multiple Products, which would be represented through a junction table.”

5. Explain how you would implement caching in a web application.

This question assesses your knowledge of performance optimization techniques.

How to Answer

Discuss the types of caching, where it would be applied, and how it would improve performance.

Example

“I would implement caching at multiple levels, including database query caching to store frequently accessed data and HTTP caching for static assets. Using a caching layer like Redis, I could reduce database load and improve response times for users by serving cached data for repeated requests.”