You will meet with many different leaders across the company in 1-hour meetings. You may be asked technical questions and could be required to do a coding exercise, but the bulk of the interview focuses on the Chewy Operating Principles PDF provided by the recruiter. Chewy has dozens of behavioral-type questions for each of the areas in that PDF, and these questions will constitute a large part of your interview. You'll be asked to describe how and why you handled certain situations in your past experiences. Each interviewer will focus on specific operating principles.

After the interview, each interviewer convenes to discuss whether you raised the bar, met the bar, or lowered the bar. This evaluation refers to the job title you applied for and how your experiences and technical skills align with those of other employees at Chewy holding the same title. For example, if you are better than half of other Data Scientist I employees within the company, you raise the bar. If you are equivalent, you meet the bar.

Chewy often seeks candidates who raise the bar, which means the threshold for the average Data Scientist I at the company is improving, making it more challenging to get hired as the bar is frequently elevated. If you are not a bar-raiser, or a good fit for the team or role you applied for, your application and interview results may be referred to another team within the company for a similar interview process.

This constantly raising bar increases the technical acumen of the team. However, it can also work against the company by making it difficult to find and retain good employees, as those who are hired are often worked very hard. During my orientation, I was told that Chewy accepts only 5% of applicants.

Questions for the Data Engineer role involved the use of windowing functions, CTEs, and cross joins.

There were three SQL questions that were quite wordy. While they did represent a good set of things to know for SQL, I didn’t end up crafting SQL statements of that complexity at the job when I worked for the company.

Most of the time, my requirements on the job were to add fields to tables and update the SQL temp tables/transformations accordingly. The interview questions had sample tables and asked you to output certain data points from joining/transforming those tables.

There were also two Python questions involving removing letters from words and using if within a try/except block.