I applied through a recruiter. I interviewed at NVIDIA (Tel Aviv-Yafo) in Oct 2025
Interview
Interview with 2 interviewers from 2 teams for 1.5 hour.
Had to describe about your previous projects, some questions about streaming and real time data ingestions,
Also Questions about data architecture.
I applied online. The process took 3 months. I interviewed at NVIDIA (Yerevan, Yerevan) in Sept 2023
Interview
I applied for the Senior Data Engineer position at the Yerevan Office on the 26th of July 2023 and got a response on the 21st of September, after 2 months (shocking). And the response was from the recruitment coordinator, saying let's have a technical interview on 4th October, (no HR, no screening, etc)
The tech interview was quite good, I felt that I made a good impression. We mostly talked about Apache Spark and its internals, with some questions about distributed systems.
The next day, another recruiter reached out to me and asked when I was ready to make a system design home assessment, and after confirming that I was okay - they sent me the task with a 2-week time limit. I did my best to complete the task, which was a quite good solution and sent it after 10 days.
A couple of days later, I got a standard message from the recruiter coordinator, saying "Unfortunately, at this time, we decided to proceed our interview process with another candidate. It is a decision we didn't make easily because you are really a strong candidate with a wonderful personality."
Mostly, my feedback is negative because of my pipe dream to work in a FANG-level company and because I've spent too much time dreaming that everything is going well during this interview process.
Interestingly, the position is still open (end of November), and even it was reposted on Linkedin.
Interview questions [1]
Question 1
Which types of query optimization do I know in Spark, and how do they work internally?
How to make a join with two big datasets, if the joining condition is not strict (expected to hear about product join and keep only relevant rows)
Other in-depth questions about spark-streaming internals, and big data techs like kafka, zookeeper, etc.