Software Engineer applicants have rated the interview process at Snap with 3.3 out of 5 (where 5 is the highest level of difficulty) and assessed their interview experience as 45% positive. To compare, the company-average is 46.1% positive. This is according to Glassdoor user ratings.
Candidates applying for Software Engineer roles take an average of 19 days to get hired, when considering 156 user submitted interviews for this role. To compare, the hiring process at Snap overall takes an average of 26 days.
Common stages of the interview process at Snap as a Software Engineer according to 156 Glassdoor interviews include:
Phone interview: 41%
One on one interview: 23%
Skills test: 15%
Presentation: 9%
Group panel interview: 4%
Background check: 3%
IQ intelligence test: 1%
Drug test: 1%
Personality test: 1%
Other: 1%
Here are the most commonly searched roles for interview reports -
Asked me a review question dealing with manipulating strings, was a pretty straightforward and fair question however, I was not able to finish since I confused myself. It was just one question in the whole review and he briefly asked me about my resume and why I was interested in working for the company.
Interview questions [1]
Question 1
Question about Strings and how to store certain aspects of the review.
One initial screening call. Then 1 coding screen. Then an on-site with 3 coding rounds and 1 system design round. All the rounds were pretty straightforward DSA patterns and SD was also a common question you read about in SD interview prep books.
For the technical rounds, I was asked leetcode style questions. Need to practice Data structures and algorithms in order to do well on the interviews. It's important to explain the code as you go along and clarfiy any questions with the interviewer.
Hard but interesting. Had to go through 1 HM round, 2 coding rounds and 2 systems design rounds. Coding round was hit counter, and message recommendation system. The System design rounds were a bit challenging.
Interview questions [1]
Question 1
Design a recommendation system for messaging to predict the next word while typing