Chewy Data Engineer interviews, as candidates describe them, center on SQL window functions, Python fundamentals, data modeling and ETL optimization, with behavioral questions and business context mixed in.
$154K
Avg. Base Comp
$195K
Avg. Total Comp
Not reported
Typical Rounds
Not reported
Process Length
Three candidates describe Chewy's Data Engineer interviews, and their experiences differ in structure. SQL is the one topic all three mention. One candidate reported window-function SQL alongside Python questions based on OOP concepts and a data modeling exercise for a Chewy warehouse. That candidate, interviewing for Data Engineer II, found the Python round tricky because it was not LeetCode-style, then went through four virtual onsite rounds, and received an offer.
Another candidate described a one-hour technical interview. It opened with a resume walkthrough, then a basic Python task of counting characters in a string, then easy SQL on joins and window functions. The interviewer dictated small tables for the candidate to write out. The candidate used a defaultdict, but the interviewer wanted a Counter, so be ready to explain why you chose a given library and its complexity.
A third candidate, interviewing for a senior role, described a recruiter screen followed by five technical interviews with different team members. Questions covered SQL and architecture, and kept returning to how to optimize an ETL process, which customer data sources feed it, and the business processes behind the pipelines. That candidate did not receive an offer.
In practice, prepare window functions and joins, Python basics you can justify, and a warehouse-style schema for a retail business. Also prepare STAR stories, since one candidate's hiring manager pushed on the examples given. Candidates did not report a consistent number of rounds, so confirm the format with your recruiter.
Synthesized from 3 candidate reports by our editorial team.
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| Question | |
|---|---|
| Customer Orders | |
| Random SQL Sample | |
| Top 3 Users | |
| Retailer Data Warehouse | |
| Monthly Product Sales | |
| Random Forest Explanation | |
| Marketing Channel Metrics | |
| Valid Anagram | |
| Hurdles In Data Projects | |
| Max Quantity | |
| Find Mismatched Words | |
| String Palindromes | |
| Client Solution Pushback | |
| Why Do You Want to Work With Us | |
| Weighted Average Sales | |
| Generative AI Privacy | |
| Empty Neighborhoods | |
| Subscription Overlap | |
| Rolling Bank Transactions | |
| Comments Histogram | |
| Closest SAT Scores | |
| Top Three Salaries | |
| Merge Sorted Lists | |
| Prime to N | |
| Experiment Validity | |
| Download Facts | |
| Last Transaction | |
| Average Quantity | |
| Monthly Customer Report |
Synthesized from candidate reports. Individual experiences may vary.
One senior-level candidate reports the process opened with a recruiter call that included some technical questions. Others do not mention a screen, so this step may vary by role and level. Prepare a concise résumé walkthrough and be ready for light technical questions.
Candidates report Python questions that were not LeetCode-style: OOP concepts for one candidate, and a character-counting task for another. The second candidate was asked why they chose defaultdict instead of Counter. Be ready to justify library choices and discuss time complexity.
SQL appears in all three reports, with window functions and basic joins mentioned most. One candidate says the interviewer dictated small tables aloud and follow-ups came after the initial query. Practice writing queries from verbally described schemas and sample data.
One candidate reports designing a data model for a Chewy warehouse. A senior-role candidate describes architecture discussions about improving an ETL process and the customer data sources it integrates. Prepare to explain schema choices, pipeline trade-offs and the business purpose of the data.
One candidate describes four virtual onsite rounds with friendly interviewers, including a hiring manager who pushed on STAR examples. A senior-role candidate describes five technical interviews with different team members. Prepare detailed behavioral stories with measurable outcomes and be ready to defend your decisions.
