I applied through an employee referral. The process took 4 weeks. I interviewed at Riverside.FM in Nov 2024
Interview
The hiring process with HR and the data team was excellent—professional, friendly, and, most importantly, efficient. It began with an HR call, followed by a technical interview with the recruiting manager, which focused primarily on SQL. Next was an interview with the analytics team lead, which had a stronger analytical emphasis. This was followed by a mostly personal interview with the VP of Data, which included one analytical use case scenario. Finally, the process concluded with a final HR discussion.
I applied online. The process took 1 week. I interviewed at Riverside.FM (Tel Aviv-Yafo) in Sep 2023
Interview
1. The interviewer called 10 minutes after the appointed time (normal practice for Israel, no big deal). During the call, there was a lot of extraneous noise, either the sounds of cooking or the sound of a person walking along a busy street. This made it difficult to catch what she was saying at all. 2. Hour of Online interview with TL via Riverside. FM platform. That was awful, the quality of the connection and video was disgusting. The picture of the TL was like one big colorful blur. Perhaps the problem was that the platform is designed for recording high-quality podcasts, and not online meetings. It was difficult to concentrate due to the large amount of interference. He first told me about the position, asked questions about my experience, and then moved on to the technical task. A dataset was given, and I had to write an analytical query. The time ran out and I didn’t have time to finish, he offered to finish it myself and send the results afterward. The team lead helped with solving the problem and it was important for him to see my train of thoughts. Based on the results of the task, detailed feedback was given. If not for the quality of communication at the meeting, it would have been a very positive interview experience.
Interview questions [1]
Question 1
The objective is to present how many daily non-test customers (n_customers) there are, showing also the split of how many were signed up (new_customers) and how many churned (the previous day was their last one).