Chewy Data Engineer Interview Questions + Guide in 2024

Chewy is a leading online retailer specializing in pet products and services, known for its customer-centric approach and innovative solutions. As a Data Engineer at Chewy, you will be instrumental in building and maintaining data infrastructure that supports analytical solutions across the company. The role involves designing and optimizing data pipelines, integrating data from various sources, and partnering with analytics teams to drive business insights.

In this guide, we’ll tackle how they conduct their interviews, along with commonly asked Chewy data engineer interview questions to help you prepare better. Let’s get started!

The interview process usually depends on the role and seniority. However, you can expect the following on a Chewy data engineer interview:

Recruiter/Hiring Manager Call Screening

If your CV is among the shortlisted few, a recruiter from the Chewy Talent Acquisition Team will contact you and verify critical details like your experiences and skill level. Behavioral questions may also be part of the screening process.

Sometimes, the Chewy Data Engineer hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will invite you to the technical screening round. Technical screening for the Chewy Data Engineer role is usually conducted through virtual means, including video conference and screen sharing. Questions in this one-hour interview stage may revolve around Chewy’s data systems, ETL pipelines, SQL queries, and problem-solving with Python.

A sample technical question might involve using SQL windowing functions, CTEs, and cross-joins. You can also expect tasks like removing letters from words in Python, which assess your basic command over the language.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds will be conducted during your day at the Chewy office, varying with the role. Your technical prowess, including programming and data engineering capabilities, will be evaluated against the finalized candidates throughout these interviews.

Apart from the coding exercises, a significant portion will revolve around behavioral questions aligned with Chewy Operating Principles. Most of it could involve discussing how you’ve handled certain situations in your past experiences. Each interviewer will focus on specific operating principles from the Chewy pdf, and at the end of the process, they’ll evaluate if you “raise the bar” for the applied position.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Data Engineer role at Chewy.

Typically, interviews at Chewy vary by role and team, but commonly Data Engineer interviews follow a fairly standardized process across these question topics.

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!