Chewy Data Engineer Interview Questions + Guide in 2025

Chewy is a leading e-commerce platform dedicated to providing exceptional pet products and services to pet owners.

The Data Engineer role at Chewy involves developing and maintaining robust data ingestion pipelines, ensuring high data quality, and fostering a collaborative environment to support the company's critical enterprise priorities. Key responsibilities include building scalable data architectures, integrating diverse data sources, and mentoring fellow data professionals on best practices. Ideal candidates will have extensive experience in data engineering, particularly with ETL processes, cloud-native data warehousing solutions, and programming languages such as Python and SQL. A strong customer-first mindset, curiosity, and innovation are essential traits that align with Chewy's values, ensuring that the Data Engineer can contribute to enhancing customer experiences and driving data-driven solutions.

This guide will help you prepare for a job interview by providing tailored insights about the role, key responsibilities, and expectations at Chewy, ultimately improving your chances of success.

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Chewy. Candidates should focus on demonstrating their technical expertise, problem-solving abilities, and alignment with Chewy's operating principles. Expect a mix of technical and behavioral questions, as the interviewers will assess both your technical skills and how well you fit into the company culture.

Technical Skills

1. Can you explain the differences between structured and unstructured data?

Understanding the types of data is fundamental for a Data Engineer, especially when building data pipelines.

How to Answer

Discuss the characteristics of structured data (e.g., organized in tables, easily searchable) versus unstructured data (e.g., text, images, videos) and provide examples of each.

Example

"Structured data is highly organized and easily searchable, typically found in relational databases, while unstructured data lacks a predefined format, such as emails or social media posts. For instance, in my previous role, I worked with both types, using SQL for structured data analysis and employing text mining techniques for unstructured data."

2. Describe your experience with ETL processes.

ETL (Extract, Transform, Load) is a core function of data engineering.

How to Answer

Outline your experience with ETL tools and processes, emphasizing any specific projects where you implemented ETL solutions.

Example

"I have extensive experience with ETL processes, particularly using Apache Spark for data transformation. In my last project, I designed an ETL pipeline that extracted data from various sources, transformed it for analysis, and loaded it into a Snowflake data warehouse, improving data accessibility for the analytics team."

3. What are window functions in SQL, and when would you use them?

Window functions are essential for performing calculations across a set of table rows related to the current row.

How to Answer

Explain what window functions are and provide a scenario where you would use them.

Example

"Window functions allow you to perform calculations across a set of rows related to the current row without collapsing the result set. For example, I used window functions to calculate running totals in a sales report, which helped the team analyze trends over time without losing the detail of individual transactions."

4. How do you ensure data quality in your pipelines?

Data quality is critical for reliable analytics.

How to Answer

Discuss the methods you use to validate and clean data throughout the pipeline.

Example

"I ensure data quality by implementing validation checks at each stage of the ETL process. This includes schema validation, data type checks, and deduplication. Additionally, I set up alerts for any anomalies detected during data ingestion, allowing for quick remediation."

5. Can you explain the concept of data modeling and its importance?

Data modeling is crucial for structuring data effectively.

How to Answer

Define data modeling and discuss its significance in data engineering.

Example

"Data modeling is the process of creating a visual representation of data structures and relationships. It's important because it helps ensure that the data architecture aligns with business requirements, making it easier to query and analyze data efficiently. I have experience creating both star and snowflake schemas for various projects."

Behavioral Questions

1. Describe a time when you had to mentor a colleague.

Mentorship is a key aspect of collaborative work environments.

How to Answer

Share a specific instance where you provided guidance and support to a colleague.

Example

"I mentored a junior data engineer who was struggling with SQL queries. I organized a series of one-on-one sessions where we worked through complex queries together, and I provided resources for further learning. By the end of our sessions, they were able to independently handle similar tasks and even contributed to optimizing our existing queries."

2. How do you handle tight deadlines and pressure?

Working under pressure is common in fast-paced environments.

How to Answer

Discuss your approach to managing stress and meeting deadlines.

Example

"When faced with tight deadlines, I prioritize tasks based on their impact and urgency. I also communicate proactively with my team to ensure everyone is aligned. For instance, during a recent project, I broke down the tasks into smaller milestones and held daily check-ins, which helped us stay on track and meet the deadline successfully."

3. Can you give an example of a time you identified a data issue?

Identifying and resolving data issues is crucial for maintaining data integrity.

How to Answer

Provide a specific example of a data issue you encountered and how you resolved it.

Example

"While working on a customer data integration project, I noticed discrepancies in customer IDs between two systems. I conducted a thorough analysis and discovered that some records were duplicated due to a data entry error. I collaborated with the data entry team to correct the issue and implemented a validation process to prevent future occurrences."

4. How do you stay updated with the latest data engineering trends and technologies?

Continuous learning is vital in the tech industry.

How to Answer

Share your strategies for keeping your skills and knowledge current.

Example

"I regularly attend webinars and workshops related to data engineering and subscribe to industry newsletters. Additionally, I participate in online forums and communities where professionals share insights and best practices. This helps me stay informed about emerging technologies and trends."

5. Describe a project where you had to collaborate with multiple teams.

Collaboration is essential for successful data projects.

How to Answer

Discuss a specific project that required cross-team collaboration and your role in it.

Example

"In a recent project to enhance our customer analytics platform, I collaborated with the marketing, sales, and IT teams. I facilitated meetings to gather requirements and ensure everyone was aligned on the project goals. This collaboration led to a successful implementation that improved our customer segmentation and targeting efforts."

Here are some tips to help you excel in your interview.

Familiarize Yourself with Chewy's Operating Principles

Before your interview, make sure to thoroughly review the Chewy Operating Principles document provided by your recruiter. This document is crucial as it outlines the core values and expectations of the company. Expect to face a variety of behavioral questions that will assess how your past experiences align with these principles. Prepare specific examples that demonstrate your alignment with these values, as interviewers will focus on how you embody them in your work.

Prepare for Technical Assessments

As a Data Engineer, you will likely encounter technical questions that test your knowledge of SQL, Python, and data engineering concepts. Brush up on your skills in writing complex SQL queries, including window functions, common table expressions (CTEs), and joins. Practice coding exercises that involve data transformations and pipeline development. Familiarity with tools like Spark, Databricks, and AWS services will also be beneficial, as these are commonly used in the role.

Emphasize Your Problem-Solving Skills

Chewy values candidates who can think critically and solve problems effectively. Be prepared to discuss specific challenges you've faced in previous roles and how you approached them. Highlight your ability to innovate and drive improvements in data processes. Use the STAR (Situation, Task, Action, Result) method to structure your responses, ensuring you clearly articulate the impact of your actions.

Showcase Your Collaborative Spirit

Collaboration is key at Chewy, as you will be working with multi-functional teams. Be ready to discuss your experience in working with diverse groups and how you’ve contributed to team success. Highlight instances where you’ve mentored others or led projects, as this demonstrates your leadership capabilities and commitment to fostering a collaborative environment.

Be Ready for a Multi-Round Interview Process

Expect to meet with various leaders across the company during your interview process. Each interviewer may focus on different aspects of your experience and skills, so be adaptable in your responses. After your interviews, the interviewers will convene to discuss your fit for the role, so ensure you leave a positive impression with each interaction.

Stay Engaged and Ask Insightful Questions

At the end of your interviews, you will likely have the opportunity to ask questions. Use this time to demonstrate your interest in the role and the company. Ask about the team dynamics, ongoing projects, and how the data engineering team contributes to Chewy's overall mission. This not only shows your enthusiasm but also helps you gauge if the company culture aligns with your values.

Reflect on Your Fit as a Bar Raiser

Chewy is known for its high standards, and they are looking for candidates who can "raise the bar." Reflect on your experiences and be prepared to discuss how you can contribute to the team's growth and success. Consider how your unique skills and experiences can enhance the capabilities of the data engineering team and help Chewy achieve its goals.

By following these tips and preparing thoroughly, you will position yourself as a strong candidate for the Data Engineer role at Chewy. Good luck!

The interview process for a Data Engineer at Chewy is structured to assess both technical skills and cultural fit within the organization. Candidates can expect a multi-step process that includes several rounds of interviews, focusing on both technical competencies and behavioral alignment with Chewy's core operating principles.

1. Initial Screening

The process typically begins with an initial screening call with a recruiter. This conversation lasts about 30-45 minutes and serves to gauge your interest in the role, discuss your background, and provide an overview of Chewy's culture and values. The recruiter will also assess your alignment with the company's mission and the specific requirements of the Data Engineer position.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted via a coding platform or through a live coding session. This assessment focuses on your proficiency in SQL and Python, as well as your understanding of data engineering concepts. Expect to solve problems related to data ingestion, transformations, and possibly work with sample tables to demonstrate your ability to write complex SQL queries. You may also be asked to explain your thought process and approach to problem-solving.

3. Behavioral Interviews

Candidates will then participate in several behavioral interviews with different team members, including potential peers and managers. These interviews are designed to evaluate how your past experiences align with Chewy's operating principles. Interviewers will ask you to provide specific examples of how you've handled various situations in your previous roles, focusing on teamwork, innovation, and customer-centric thinking. Each interviewer will concentrate on different principles, so be prepared to discuss a range of topics.

4. Final Interview

The final stage of the interview process typically involves a more in-depth discussion with senior leadership or a technical lead. This interview may include a mix of technical questions and strategic discussions about your vision for data engineering and how you can contribute to Chewy's goals. You may also be asked to present a case study or a project you've worked on, showcasing your technical skills and ability to communicate complex ideas effectively.

5. Decision and Feedback

After all interviews are completed, the interviewers will convene to discuss your performance and determine if you meet the "bar" for the role. Chewy places a strong emphasis on hiring candidates who can raise the bar for the team, meaning they are looking for individuals who not only meet the requirements but also bring additional value to the organization. Candidates will receive feedback on their performance, regardless of the outcome.

As you prepare for your interview, consider the types of questions that may arise during this process.

1. Write a SQL query to select the 2nd highest salary in the engineering department.

Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

2. Create a function to parse the most frequent words used in poems.

Create a function to parse the most frequent words used in poems. Return a dictionary of word frequencies, with keys as the frequency and values as lists of words. Process all words as lowercase and ignore punctuation.

3. Create a function to find words not common to both sentences.

Create a function that returns a list of all words that are not in both sentences. Assume no punctuation, extra tabs, or spaces, and treat words case-insensitively.

4. What metrics would you use to determine the value of each marketing channel?

Given all the different marketing channels and their respective costs at Mode, a B2B analytics dashboard company, what metrics would you use to evaluate the value of each marketing channel?

5. How would you measure the success of the banner ad strategy?

An online media company wants to experiment with adding web banners into the middle of its reading content to monetize web traffic. How would you measure the success of this banner ad strategy?

6. What do you recommend for a $1 million direct mail program, and how will you measure its impact?

Your team wants to invest $1 million in a direct mail program for the first time. What do you recommend for both the short and long term, and how will you measure the direct impact of this investment?

7. How does random forest generate the forest and why use it over logistic regression?

Explain the process of how random forest generates multiple decision trees to form a forest. Additionally, discuss the advantages of using random forest over logistic regression, such as handling non-linear relationships and providing feature importance.

8. Which model would perform better for predicting Airbnb booking prices: linear regression or random forest regression?

Consider building a model to predict booking prices on Airbnb. Compare the performance of linear regression and random forest regression, taking into account factors like model complexity, ability to capture non-linear relationships, and interpretability.

9. How would you interpret coefficients of logistic regression for categorical and boolean variables?**

Explain how to interpret the coefficients of logistic regression when dealing with categorical and boolean variables.

10. What is the difference between covariance and correlation? Provide an example.

Describe the difference between covariance and correlation, and provide an example to illustrate the distinction.

11. What are time series models? Why do we need them when we have less complicated regression models?

Explain what time series models are and why they are necessary despite the availability of simpler regression models.

12. How would you determine if the difference between this month and the previous month in a time series dataset is significant?

Given a time series dataset grouped monthly for the past five years, describe how you would assess if the difference between this month and the previous month is significant.

13. How would you address a manager’s complaint about a packet filling machine not functioning correctly?

A manager reports that a packet filling machine, which aims to place 25 packets into a box, is malfunctioning. Customers are complaining about incorrect packet counts. How would you investigate and resolve this issue?

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Chewy data engineer interview include:

• Be SQL Ready: Focus on mastering SQL, as many complex queries, including windowing functions and CTEs, are part of the technical screening.
• Understand Chewy’s Operating Principles: Chewy’s behavioral questions are aligned with their operating principles. Prepare to articulate your experiences in a way that demonstrates how you adhere to these principles.
• Prepare for Python Challenges: Brushing up on basic to moderate Python problems will be useful as Python-specific technical questions are also part of the interview.

What is the average salary for a Data Engineer at Chewy?

What kind of technical skills are required for a Data Engineer at Chewy?

You need strong expertise in SQL and Python, experience with cloud environments like AWS, and a solid understanding of data integration and pipeline construction. Familiarity with tools like Databricks, Kafka, and Snowflake is also essential.

What are some of the main responsibilities of a Data Engineer at Chewy?

You’ll be responsible for designing, developing, and maintaining data architecture and pipelines. This includes creating data products, managing SSOT tables and data marts, and collaborating with various teams to provide data solutions. Mentorship and leading the deployment of emerging tools are also part of the role.

Chewy offers a dynamic and challenging environment for prospective Data Engineers. The interview process is rigorous, encompassing technical assessments, behavioral interviews, and discussions centered on Chewy’s Operating Principles. Evaluating candidates on their ability to “raise the bar” ensures a continuous improvement culture.

If you’re ready for a fast-paced, intellectually stimulating career at Chewy, we recommend preparing thoroughly for your interview. For more insights, check out our main Chewy Interview Guide. This guide covers potential interview questions and tips tailored to Chewy’s hiring process. Additionally, explore our other interview guides for roles like Software Engineer and Data Analyst to get a broader understanding of Chewy’s interview landscape.

Good luck with your interview!