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. Joining Chewy's Data Engineering team means embracing a fast-paced environment with continuous opportunities to enhance data-driven decision-making processes, contributing to a world-class e-commerce experience.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Chewy as a Data Engineer. Whether you were contacted by a Chewy recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

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

In some cases, 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 present you with an invitation for the technical screening round. Technical screening for the Chewy Data Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long 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, varying with the role, will be conducted during your day at the Chewy office. 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. The bulk 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.

Quick Tips For Chewy Data Engineer Interviews

• Should contain three tips for interviewing for this specific company based on interview experiences.

Example:

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 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.

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

• 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.

• 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.

• 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.

• 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?

• 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?

• 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?

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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?

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

Q: What is the interview process like for the Data Engineer position at Chewy? The interview process typically starts with a phone call with a recruiter, followed by a series of technical and behavioral interviews. These are around 45 minutes each and cover areas such as SQL, Python, and behavioral questions based on the Chewy Operating Principles. You may also be given a coding exercise.

Q: 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.

Q: What types of behavioral questions can I expect during the interview? Behavioral questions are centered around Chewy's Operating Principles. You might be asked to describe how you handled specific situations in the past, such as dealing with constructive criticism or tackling a project with limited resources.

Q: 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.

Q: How can I best prepare for a technical interview at Chewy? Practice common SQL and Python problems, such as those available on Interview Query. Be prepared for complex SQL queries involving joins and transformations. Also, familiarize yourself with the Chewy Operating Principles, as these will guide many of the behavioral questions.

Conclusion

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.

The role of a Data Engineer at Chewy is multifaceted, requiring expertise in SQL, Python, data architecture, and cloud technologies like AWS and Snowflake. Successful candidates will thrive in collaborating with analytics teams, leading technology implementations, and driving data-driven decision-making across the organization.

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.

At Interview Query, we're committed to equipping you with the tools, confidence, and strategies to excel in your interview journey. Don't miss out on our comprehensive resources and company-specific guides for better preparation.

Good luck with your interview at Chewy!