I generally enjoy interesting, open, and dynamic conversations and problem-solving, so the whole recruitment process—starting from the initial chat with HR up to the last conversation with the head of data and the potential manager—was a very pleasant one.

First of all, the recruiter managing my case was always very open and friendly, and at the same time, very engaged and proactive in helping me approach the main interviews with the right expectations.

Also, I never had to wait more than a day or two to hear detailed feedback from the interviewers. The feedback was not just a one-sentence conclusion but always a detailed evaluation with an honest judgment and improvement suggestions. I even managed to squeeze in some curiosity-driven follow-ups in one case.

The homework, as well as the ad-hoc technical interview, were tuned towards testing domain-specific knowledge, familiarity with best practices, and general coding culture. They were not too complicated but were to be taken seriously.

The systems-design interview was a lot of fun, and I would have liked to actually get a chance to implement the system we've been interactively designing with the interviewer. The interviewer was very open to building on my propositions rather than pushing for a model solution, which made the process much more two-sided and interactive!

In all the interviews, I had ample opportunity to ask questions about Revolut, both technical and personal. The highlight was the final chat with the head of data and my potential future line manager, where we had a very lively discussion about where data engineering and analytics are headed at Revolut, how that relates to our personal experiences in other companies, and what lessons we have all learned from that.

All in all, even if the interviewing process had not led to me receiving an offer, I would have never considered it time wasted but rather just a very pleasant experience for free :)

Homework:

• Straightforward SQL
• Medium-level Python

Technical Interview:

• Python basics
• Some slightly-above-cliché data science-level concepts

Systems Design:

Design a specific high-throughput analytics-supporting microservice (ad-hoc interactive conversation, so it could end up anywhere, I presume).