Software Engineer candidates at Shopify report an online assessment, pair programming or live coding on practical problems, a deep dive or system design round for some, and a Life Story interview.
$148K
Avg. Base Comp
$200K
Avg. Total Comp
Not reported
Typical Rounds
3-4 weeks
Process Length
Candidates describe Shopify's Software Engineer loop as part screening, part hands-on coding, and part conversation about who you are. Before any live round, several report an online assessment that mixes an aptitude or IQ-style test with a personality or culture questionnaire. One candidate faced a 144-question workplace assessment, a 40-question aptitude test in 20 minutes, and three timed coding problems that called for C++ and Ruby alongside a language of their choice.
The live technical rounds center on pair programming on hands-on coding problems. Reported prompts include an LRU cache, which one candidate was asked to solve twice, a class-based solution to a data problem using a fairly complex data structure, a robot that moves in different directions, and a URL shortener where the interviewer seemed to expect an end-to-end solution. One candidate was allowed to use AI during the technical interview, and the interviewers seemed interested in how they incorporated it while coding.
Beyond that, accounts diverge. Some describe a technical deep dive on a past project and a system design round, with prompts like a library check-in system or a webhook delivery design covering retries and idempotency. The Life Story interview shows up in many reports: a walk through your career, why you made each move, and why Shopify. Feedback after one rejection pointed to needing more depth in problem solving and more measurable outcomes, and reported timelines run about three weeks to a month. Rehearse narrating design choices aloud, quantify your impact, and keep a specific answer ready for why Shopify.
Synthesized from 53 candidate reports by our editorial team.
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Featured question at Shopify
Given two sorted lists, write a function to merge them into one sorted list.
| Question | |
|---|---|
| Rolling Bank Transactions | |
| Prime to N | |
| Upsell Transactions | |
| Total Spent on Products | |
| One Element Removed | |
| Paired Products | |
| Alphabet Sum | |
| Identifying User Sessions | |
| Resumable Fact Table Load | |
| Clickstream Data | |
| Hurdles In Data Projects | |
| Priority Queue Using Linked List | |
| Move Zeros Back | |
| Messenger Service Design | |
| Walking Robot | |
| Filling Supermarket Bag | |
| Yelp-like System | |
| A/B Testing a Checkout Button Change | |
| String Palindromes | |
| SageMaker Deployment Architecture | |
| International e-Commerce Warehouse | |
| Liker's Likers | |
| Text Editor With OOP | |
| Fixed-Length Arrays: Deletion | |
| Minimum Directional Path | |
| External Sorting | |
| Pop Tail | |
| Client Solution Pushback | |
| Unified Event Pipeline |
Synthesized from candidate reports. Individual experiences may vary.
Several candidates describe an early recruiter or HR call that explains the process and answers questions, sometimes after a resume screen. Others report the Life Story conversation near the start instead of the end, so the exact order varies. Confirm the sequence with your recruiter.
Candidates report an aptitude or IQ-style test plus a personality or culture questionnaire. One described 144 workplace questions, then 40 aptitude questions in 20 minutes, then three timed coding problems across languages including C++ and Ruby. Another described a CCAT with 40 questions in 20 minutes plus a CoderPad with three medium LeetCode-style problems. Timed, mixed-format practice fits these reports.
Candidates report one or two pair-programming or live coding sessions. Reported problems include an LRU cache (asked twice to one candidate), a class-based solution to a data problem, a robot moving in directions and a URL shortener. One candidate was allowed to use AI during the technical interview. Thinking aloud and structuring the solution clearly mattered in these reports.
Some candidates describe a deep dive on a system they worked on, explaining how each part worked. Others describe system design prompts such as a library check-in system or a webhook delivery design with retries and idempotency. Not every candidate reported reaching these rounds.
Many candidates describe a conversation about their career path, why they made each move, and why they want Shopify. Some found it conversational, others said it felt subjective. One candidate was also asked to revisit topics from their first screening in this round.
