Wise Interview Questions
& Process

The interview process involved a Recruiter/Talent Screen as the first stage, followed by a Hiring Manager + Engineering Lead interview. The first stage was pretty basic. In the second stage, I was asked to discuss a theoretical problem for half the time and then something I had previously worked on. The interview felt poorly organized, and the engineer on the call had a rude attitude and poor interpersonal skills. Afterwards, I got an automated email saying I didn't move forward, which is fine, but candidates should receive actual feedback instead of a generic email. If you take the time to interview someone, you should take the time to provide candidate feedback, or else don't hire! No relevant feedback was provided.

I applied online and after about two weeks a recruiter reached out to me. I scheduled an interview. When I joined the meeting link at the scheduled time, the recruiter didn't show up for about 5 minutes, so I emailed him. He replied that I had missed the interview, which wasn't true as I was in the meeting for around 20 minutes. I had read some reviews about recruiters ghosting candidates here, so I was surprised when it happened to me. He later acknowledged it might have been an error on his end and asked me to book another slot. That was a bit of a red flag for me. The second time, he did show up, but the interview felt quite cold and only lasted around 20 minutes. I applied for this role in early January and as of mid-March it’s still being advertised, which made me question how actively it’s being filled. The job description itself covers what feels like 3–6 roles. The responsibilities span core data analysis (reporting, dashboards, SQL), analytics engineering (building and maintaining data pipelines), marketing attribution ownership, and elements of data science such as experimentation and marketing mix modelling. On top of that, there’s an expectation to influence budget allocation and act as a strategic partner to marketing stakeholders. I realized that this was a “unicorn marketing measurement role”. Not quite data science, not just analytics, not just engineering, not just marketing. But you are expected to cover all of them well enough. I say this because a line from the job description read "own the code, define attribution logic, partner with teams, guide strategy". While that level of scope might appeal to some, it felt overly broad to me and raised concerns.

The process included several stages, starting with CV screening, then a recruiter interview, a technical interview, and finally a discussion with senior team members. It took a lot of time. The main issue was that the critical requirement for deep platform domain experience was only revealed late in the process, just before the final stage. Earlier stages didn't make it clear that platform-specific leadership experience was essential for this role. Feedback at the end emphasized platform domain depth and perspective, which could have been assessed earlier. This meant a candidate could invest significant effort before discovering a fundamental mismatch in expectations.

The Wise interview process kicks off with a timed HackerRank assessment. This usually has three parts: a LeetCode-style coding challenge, often involving sliding windows or arrays, a SQL query that requires joins and aggregations, and a task to filter a REST API. If you do well on that, you'll then tackle the Maki People assessment. This involves quick matching tasks for attention to detail, multiple-choice questions on software engineering basics, and a few one-way video recordings. In these videos, you'll need to show how you embody 'Wise values' like customer advocacy and autonomy, using the STAR method. Passing these automated steps means you'll get to live technical interviews where you'll do pair programming and system design discussions.