I applied online. The process took 3 weeks.
It was a pretty standard FAANG interview process. It started with an initial call with the recruiter. The recruiter was professional and laid out the expectations of the interview process and discussed the compensation band. The first round was an initial technical screen: 90 minutes, LeetCode + SQL (Medium).
After the initial technical screen, there was the onsite that had four rounds:
The onsite technical round was harder than the screen technical round. I would say it tended to be closer to LeetCode Hard than LeetCode Medium. Overall, the process seemed to be very fair. The hiring manager was great: they shared insights about the scope of the role and got me super excited for the opportunity. The recruiting team was very flexible in getting multiple rounds scheduled, and the experience was frictionless.
The hiring process was standard. The technical round included DSA and SQL problems that were very similar to what you find on LeetCode but with some custom twists. Data Modelling was to model a use case to solve for a certain query pattern. Systems design tended towards designing a data platform. The hiring manager round was more of a behavioral and experience discussion, along with Lyft values.
The following metrics were computed from 4 interview experiences for the Lyft Data Engineer role in Toronto, Ontario.
Lyft's interview process for their Data Engineer roles in Toronto, Ontario is extremely selective, failing the vast majority of engineers.
Candidates reported having very good feelings for Lyft's Data Engineer interview process in Toronto, Ontario.