Chewy Software Engineer Interview Process & Question Guide

Chewy’s software engineer interview process is designed to find candidates who can help build and support the technology behind their popular pet supply business. As an engineer, you’ll be working on projects that impact millions of customers and help Chewy deliver a great experience for pet parents.

This guide will walk you through what to expect at each stage of the interview, from the first recruiter call to technical and behavioral rounds. You’ll also find tips on how to prepare for common questions and show your skills, so you can feel confident and ready for your Chewy interview.

Why Work at Chewy as a Software Engineer?

Chewy’s engineering team is focused on solving problems like large-scale order fulfillment, recommendation systems for pet products, and building resilient infrastructure that supports fast and reliable delivery across the country. You’ll work with modern technologies like Java, Spring Boot, AWS, and React in an agile, collaborative environment.

What makes Chewy stand out is the blend of mission and innovation: engineers are not only expected to write great code, but to think critically about how that code helps pet parents. The company’s culture promotes ownership, rapid experimentation, and putting customers first—making it a great place to grow your career while building products that matter.

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

Applications and Recruiter Screen

To start, you’ll either submit your application online or get contacted by a recruiter. The first step usually includes a quick 30-minute call with the recruiter. They’ll ask about your background, your resume, your experience with certain technologies, and why you’re interested in working at Chewy. You can expect questions about your current or past projects, your programming skills, and how familiar you are with tools like React, Java, or Spring. They’ll also touch on basics like your salary expectations and when you can start.

Technical Interviews

Next comes the technical part of the process. It usually begins with a live coding test on platforms like HackerRank. These tests focus on data structures, algorithms, and core programming concepts, often using Java or JavaScript. You might be asked to solve problems with arrays, binary search trees, or 2D logic.

If you do well here, you’ll move on to a final round of four one-hour interviews. These include a live coding session with an engineer, a system design interview where you’ll plan out things like databases and APIs, a debugging interview to fix broken code and explain your testing methods, and finally, a behavioral interview to talk about your teamwork, past experiences, and how you solve problems.

Onsite/Final Round

The final interview round might be in person or virtual and includes a panel of different team members, such as hiring managers and technical leads. Each part of the interview focuses on a different skill—coding, system design, debugging and testing, and your fit with the team’s culture. You may also get questions about how you learn new technologies and how comfortable you are with the tools and tech used at Chewy.

Feedback & Offer

After the interviews, you’ll get feedback on how you did. This might include notes on your technical strengths or areas where you may need to improve, like adjusting to new technologies. If things go well, you’ll get an offer! If not, don’t worry—you might be encouraged to apply for other roles at Chewy or be given helpful tips for the future.

Landing a Software Engineer role at Chewy means proving you can build resilient, pet-centric e-commerce solutions—from real-time inventory management to personalized recommendation engines—that delight millions of pet parents every day. The interview is split into three focused sections—Coding/Technical, System/Product Design, and Behavioral/Culture-Fit—to assess your problem-solving chops, architectural vision, and alignment with Chewy’s customer-first, pet-obsessed culture.

Coding / Technical Questions

As a Chewy Software Engineer, you’ll tackle algorithmic challenges and hands-on coding tasks in Java, Python, or Go, with a focus on data structures, concurrency, and performance optimization for RESTful microservices. Expect problems around order-routing algorithms, in-memory caching with Redis for hot product reads, and real-time inventory updates via pub/sub or WebSocket patterns. Solutions should reflect production-grade best practices—modular design, comprehensive unit tests, and efficient O-notation—to mirror Chewy’s CI/CD pipelines running on Docker and Kubernetes.

1. Implement a priority queue using a linked list

   Scheduling background tasks—like image processing for product photos or order-fulfillment jobs—can use a custom priority queue. Build insert, delete, and peek by maintaining sorted nodes based on priority. Analyze time complexity and compare to heap-based implementations. Designing specialized data structures demonstrates your ability to tailor solutions to Chewy’s domain needs.

2. Implement a function in Java to detect if a linked list contains a cycle.

   This question assesses your proficiency with fundamental data structures and algorithms, specifically pointer manipulation and efficient traversal techniques. Detecting cycles is essential in preventing infinite loops and ensuring data integrity, which is critical in backend services managing Chewy’s large-scale inventory and order processing systems. You should implement Floyd’s cycle-finding algorithm to achieve optimal time and space complexity.

3. Find the total number of unique conversation threads

   Similar to grouping customer-support chats or user reviews into distinct threads, this problem can be solved with union-find or graph traversal. Explain how you’d process streaming events with limited memory, using path compression or incremental clustering. Discuss trade-offs between one-pass algorithms and multi-pass analysis. This tests your ability to build scalable logic for real-time user interaction analytics.

4. Implement a function in Go to find the nth Fibonacci number using memoization.

   This task evaluates your ability to manipulate arrays and merge datasets efficiently, a common operation in managing sorted data streams like product catalogs or transaction logs. Efficient merging is crucial in systems that require quick data aggregation and updating, such as syncing inventory across warehouses. Your solution should demonstrate a linear time approach using two pointers, reflecting the type of optimization expected in Chewy’s backend infrastructure.

5. Find the missing integer from a array of 1 to N

   This mirrors catching gaps in order IDs or shipment sequences in Chewy’s fulfillment systems. You can use the arithmetic sum formula or XOR trick to run in O(n) time with O(1) extra space. Be sure to discuss integer overflow in Java or C++ when N is large, and handle edge cases like empty arrays or duplicates. Clean, production-ready code here reflects Chewy’s emphasis on reliability in microservices.

6. Search for a value in log(n) over a sorted 2D matrix

   Think of this as looking up a product SKU in a grid indexed by category and subcategory. Use binary search on both dimensions (or map 2D indices into a 1D array) to achieve logarithmic complexity. Clarify your assumptions about row/column ordering, and handle empty or jagged matrices gracefully. This showcases how you’d optimize inventory or pricing lookups in Chewy’s backend.

7. Implement a function in Go to find the nth Fibonacci number using memoization. This problem tests your grasp of recursion and dynamic programming techniques, which help optimize computations by avoiding redundant calculations. Such skills are valuable in Chewy’s microservices architecture, where performance and resource efficiency are paramount, especially in services that compute metrics or predictions on the fly. Your solution should balance readability with efficiency, showing your capability to write scalable and maintainable Go code.

8. Write a function to rotate an array by k positions

   Rotation emulates circular buffers for Chewy’s order-event streams or sensor logs in warehouses. Implement it in-place via subarray reversals or calculate new indices with mod arithmetic for O(n) time and O(1) space. Consider large k relative to array length and cover corner cases like k = 0 or k > n. Efficient, low-overhead utilities like this are staples in Chewy’s data pipelines.

9. Justify an array of words given an array of widths

   This is like formatting product descriptions or notification banners on various device screens. Craft a greedy line-packing algorithm that respects width limits, carefully distributing spaces to avoid overflow. Handle edge cases—very long words, single-word lines, or zero-width constraints. Precise control-flow here reflects Chewy’s commitment to a polished user experience.

10. Implement a basic LRU cache

    Caching pet profiles, inventory lookups, or session tokens in Chewy’s microservices demands an LRU cache with O(1) get/put. Combine a hash map with a doubly linked list to track usage order, and discuss how you’d make it thread-safe under concurrent access. Explain eviction behavior under high read/write loads. Robust caching solutions like this keep Chewy’s services fast and reliable.

11. Write a Go program that reads a stream of integers and outputs the median after each new integer is added.

    This question measures your ability to implement real-time data processing algorithms, often using advanced data structures like heaps or balanced trees. At Chewy, real-time analytics and monitoring of user behavior, inventory levels, or order flows require such streaming computations. Your implementation should be optimized for fast insertions and median retrievals, demonstrating readiness to build responsive and scalable systems within Chewy’s technology stack.

System / Product Design Questions

Chewy’s platform must seamlessly handle peak demand during flash sales and subscription renewals while maintaining 99.99% uptime. In this segment, you’ll design end-to-end architectures: scalable microservices on Kubernetes, event-driven pipelines with Apache Kafka or AWS Kinesis, distributed database sharding using DynamoDB or Aurora, and CDN-backed content delivery for pet catalog assets. Be prepared to justify trade-offs between consistency and availability for inventory lookups, outline autoscaling strategies under heavy load, and propose observability stacks (Prometheus, Grafana) to ensure reliable, low-latency user experiences.

1. Improving the performance of document app’s autosave feature to handle data processing systems

   To enhance the autosave feature, consider implementing a more efficient data buffering system to batch writes, reducing the frequency of disk access. Additionally, explore using faster storage solutions or distributed systems to handle the increased load, and optimize the data processing pipeline to minimize latency.

2. Describe the architectural style you use for backend systems.

   When describing the architectural style for backend systems, consider discussing microservices, monolithic, or serverless architectures. Highlight the trade-offs in scalability, maintainability, and deployment complexity. Discuss how the choice of architecture aligns with the system’s requirements and constraints.

3. Design a Parking Application System that supports users finding parking spots leased from various organizations across a city.

   As a data engineer, your task is to design the backend system that manages the availability and reservation of parking spots. Consider how you would ingest and normalize raw spot data, handle real-time updates, and provide efficient queries for location-based searches. Address challenges around data freshness, concurrency control for reservations, and integration with partner APIs. Your design should ensure scalability, low latency, and high availability to support a seamless user experience, reflecting the kind of complex data engineering Chewy requires for its high-demand platform.

4. Investigate and optimize slow OLAP aggregations for a large retail company struggling with monthly and quarterly inventory reports.

   This question evaluates your understanding of OLAP system design and performance tuning, essential for managing Chewy’s large-scale product and inventory data. Explore possible bottlenecks such as inefficient aggregation queries, lack of materialized views, or suboptimal partitioning. Discuss aggregation strategies like precomputed rollups, incremental updates, or cube technology. Highlight how you’d monitor performance, identify query patterns, and balance storage costs with reporting speed, showcasing skills critical for Chewy’s data warehouse efficiency.

5. Design the database schema and optimizations for a swiping app similar to Tinder.

   Focus on modeling user profiles, swipe interactions, and match data efficiently. Address indexing strategies, handling high write volumes from swipes, and ensuring fast lookups for mutual matches. Consider data partitioning, caching mechanisms, and the use of NoSQL or graph databases for relationship queries. This question aligns with Chewy’s needs to design performant, scalable systems that support dynamic user interactions in a pet e-commerce or community platform context.

Behavioral or “Culture Fit” Questions

At Chewy, culture fit means putting pets and their parents first, collaborating across functions, and iterating rapidly in a dynamic e-commerce environment. You’ll share stories—resolving production incidents during a site-wide sale, partnering with product managers to refine the checkout flow, or iterating on a personalized recommendation feature based on customer feedback. Focus on clear STAR-style storytelling, metrics-driven outcomes (e.g., reduced cart abandonment, improved page load times), and how you embodied Chewy’s core values: caring deeply for customers, embracing continuous learning, and building with integrity.

1. Tell me about a time you received tough feedback. What was it, and what did you do afterward?

   In Chewy’s pet-obsessed culture, constructive feedback drives continuous improvement and customer delight. Describe a moment when a peer or lead challenged your approach—perhaps calling out maintainability issues in your order-routing service or noting gaps in sprint communications. Explain how you acted on that feedback: did you refactor critical modules, introduce new CI checks, or seek mentorship to improve collaboration?

2. Describe a data project you worked on. What were some of the challenges you faced?

   At Chewy, engineers own features end-to-end—from personalized pet recommendations to high-throughput fulfillment pipelines. Choose a project such as building a real-time inventory sync system or a recommendation engine for pet supplies, and outline the technical hurdles: telemetry gaps across microservices, intermittent latency spikes under peak load, or data consistency issues in a distributed database. Walk through how you dove into logs, traced requests through Kubernetes, and partnered with DevOps and QA to implement monitoring, retries, or schema fixes.

3. Tell me about a time you faced a conflict while working on a team. How did you handle it?

   To answer this question, describe a specific situation where you encountered a conflict with a team member. Explain the steps you took to address the issue, focusing on communication and collaboration to reach a resolution. Highlight the outcome and any lessons learned from the experience.

4. How do you prioritize your tasks when working on multiple projects?

   To prioritize tasks when working on multiple projects, assess the urgency and importance of each task, and consider deadlines and the impact on overall project goals. Use tools like priority matrices or to-do lists to stay organized and ensure that critical tasks are completed first.

5. How do you communicate technical concepts to non-technical stakeholders?

   Provide a specific example of a time you successfully communicated a technical concept to a non-technical audience. Describe the strategies you used to ensure understanding, such as using visual aids, simplifying language, or focusing on the business value.

Preparing for a software engineering role at Chewy requires a blend of technical proficiency, understanding of the company’s domain, and alignment with Chewy’s fast-paced, customer-obsessed culture. Here’s a comprehensive guide based on recent candidate experiences and Chewy’s stated expectations:

Understand the Domain

Before your interview, take some time to learn about Chewy and what they do. Chewy is a big online store that sells pet products, and they really focus on making things easy and convenient for pet owners. It helps a lot to understand how large e-commerce websites work and the challenges they face with lots of users and orders. If you have experience with technologies like Java with Spring, AWS cloud, microservices, or secure login systems, be sure to highlight that since these are common in e-commerce.

Simulate Interview Conditions

Chewy’s technical interviews usually start with a timed coding test on platforms like HackerRank. These tests focus on object-oriented programming, data structures, and algorithms, mostly in Java. To prepare, practice solving medium-level coding problems while timing yourself, just like in the real interview. Also, practice explaining your thinking out loud because clear communication is really important during the interview.

Use Real-World Scenarios

Expect some questions about designing systems that fit real-world needs in e-commerce. For example, you might be asked how to create a scalable notification system or a secure way to handle user logins. Be ready to talk about how you would make your designs grow with more users, stay safe, and fit Chewy’s goal of improving customer experience. Use examples from your past jobs or projects to make your answers stronger.

Mock Interviews

Doing mock interviews is a great way to build confidence. Practice answering both technical questions and behavioral ones, where you talk about your teamwork, leadership, or problem-solving skills. For the behavioral parts, try using the STAR method—explain the Situation, the Task you faced, the Actions you took, and the Result you got. If you can, try mock interviews on websites like Interview Query, which even has practice material specifically for Chewy.

Research Chewy Software Engineer Teams and Culture

It’s helpful to learn about how Chewy’s engineering teams work and what the company culture is like. They move fast, focus on teamwork, and care a lot about delivering good results quickly. They believe in prioritizing important work, moving fast, and learning quickly from mistakes. Find out about their tech stack, recent projects, and values like “Customer First” and “Earn Trust.” During your interview, be ready to talk about how you fit into this kind of environment and show your enthusiasm for Chewy’s mission.

Got questions about the interview process at Chewy? This section covers common ones to help you feel more prepared and at ease.

How much is the salary of software engineers at Chewy?

What languages and technologies should I focus on?

Java is heavily emphasized, but experience with Spring, AWS, Jenkins, Terraform, and e-commerce systems is a plus. For frontend roles, React and TypeScript may be relevant.

What is the structure of the technical interview?

Expect a mix of coding challenges (often on HackerRank), object-oriented design, system architecture questions, and debugging scenarios. You may also be asked to discuss your previous work and design decisions.

How important are behavioral interviews?

Very important. Chewy values teamwork, customer focus, and adaptability. Use the STAR method to structure your answers and highlight your fit with Chewy’s culture.

How much time should I spend on system design?

Allocate at least a few days to practice system design basics, as you’ll likely face questions about building scalable, reliable systems.

Preparing for a software engineering interview at Chewy can be demanding, but with the right strategy and practice, you can approach each step with confidence. By reviewing common technical, system design, and behavioral questions—and understanding the unique aspects of Chewy’s process—you’ll be better equipped to showcase your skills and stand out as a candidate.

For even more targeted preparation, Interview Query is an excellent resource. Explore our Python Interview Learning Path, review real Python data science interview questions, and get inspired by Nathan Fritter’s success story. Whether you’re tackling coding challenges or brushing up on system design, these resources will help you prep with purpose and confidence.