I interviewed at Squarepoint Capital (Montreal, QC)
Interview
3 rounds of onsite (back in May 2023) 2 coding rounds which include trivia. 1 behavioural but they dive into your previous experiences on your resume pretty in depth. There was a technical OA before all this.
Interview questions [1]
Question 1
A question to iterate through a complex dictionary object (kind of like a json object) and collect certain items and group them in a time efficient manner
I interviewed at Squarepoint Capital (Montreal, QC)
Interview
[SECOND ROUND INTERVIEW]
60-minute live coding session on HackerRank
One engineer observing and interacting with you throughout
You get a file-like string input that simulates a messy CSV (not just an array or list)
First, you need to write code to robustly parse and clean the data
You'll clarify weird formatting and ask questions as needed—the interviewer will give details if you ask
Must process the data as a stream, not reading the whole thing into a DataFrame by default
After parsing, you’ll answer a set of concrete business queries based on the cleaned data
At the end, there are a few minutes for your own questions
Interview questions [1]
Question 1
Interview Questions:
Count how many employees have a salary strictly over 30,000
Identify which employee has held their job the longest (using start and end dates—treat missing end dates as “today” and missing start dates as “skip”)
Find the employee with the second-highest salary
You may need to decide how to handle missing names (recommended: use a placeholder like “(unknown)” so the row isn’t dropped for the other queries)
Clarify how to deal with rows that are malformed, have extra or missing fields, or contain blank lines
Explain your data structures and approach as you go (especially performance and edge cases)
Option to use native Python types and modules; pandas probably not available
You don’t have to provide a tie-breaking rule unless asked, but if you do, picking the lexicographically smallest name is reasonable
Overall, strong candidates can:
Parse and clean up messy, inconsistent tabular input
Make and explain reasonable real-world assumptions
Use Python constructs like csv.reader, datetime.fromisoformat, and simple lists/dicts
Communicate their approach and edge-case handling out loud
