J'ai postulé en ligne. Le processus a pris 3 semaines. J'ai passé un entretien chez Nansen.ai
Entretien
Stages:
1. Screening call with recruiter to talk about background and salary expectations.
2. Interview with Head of Data
3. Take home assignment which was to be completed in a week.
4. Presentation of assignment which included questions about your implementation and some live coding questions related to the assignment
5. Interview with Head of People and Engineering Manager of the team you will be working in.
J'ai postulé en ligne. Le processus a pris 2 semaines. J'ai passé un entretien chez Nansen.ai en nov. 2025
Entretien
1. HR screening call
2. VP Engineering Interview
3. Technical Test (take home with AI allowed)
4. Technical Panel Discussion of technical test approach
5. Engineering Manager Panel Discussion
Lots of communication with hiring manager throughout the whole process, focused interviews with clear agenda, the whole process took around 2 weeks from first contact to offer
Questions d'entretien [1]
Question 1
I had to use public blockchain data and design an address labelling pipeline. Then discuss my approach in the technical panel discussion. Only one component of the pipeline had to be coded (ingestion, transformation, db schema or API).
I was also asked to write a SQL query live in the technical panel to show my competence
J'ai postulé en ligne. Le processus a pris 3 semaines. J'ai passé un entretien chez Nansen.ai (Kuala Lumpur) en sept. 2022
Entretien
1. Screening call, recruiter is some of the nicest guys I have met 2. Interview with Staff Data Engineer 3. Technical Assignment 4. Assignment review The reason for my rejection has been the strangest one I have ever had due to overpowering their expectations? When your work is at actual production quality, it concerns them rather than recognizing it being a difference in engineering practices which I thought it should have been another round of discussion for that. What worse is, strong opinions like this was not even raised by them during / after the interview and their preference over a full jupyter notebook solution over a proper code base. Small little things like this speak a lot about their engineering culture and maturity.