Sanofi Data Engineer Interview Guide

Candidates describe Sanofi's data engineer process as a mix of hands-on data questions and conversations about past projects. One candidate reports a recruiter call, then a technical round drawn largely from the resume. It included two SQL problems, one on filtering across several conditions with a follow-up on making the query faster, and one on total and average sales per region. The same round covered PySpark work such as finding duplicates in a dataframe and moving them to another frame, plus basic Python list reversal and slicing. There were also descriptive questions on normalization, architecture and star versus snowflake schemas. A second round with a manager focused on why past architectures were chosen, how to handle production changes that affect downstream pipelines, and how to manage live failures. It ended with an HR discussion.

A different candidate reports a 45-minute in-person manager interview with no coding. It covered current work, how to respond when the business asks for a solution that isn't suitable, and again star versus snowflake schemas. Another candidate in the Digitalent program describes a video screening and coding game, an HR call, a hiring manager technical interview and a final round with program managers, with a behavioral question about a time they failed and recovered.

Two of these candidates describe slow communication, and reported timelines ran from about 1.5 months to nearly three months. Plan to explain your own pipeline decisions clearly, practice SQL optimization, and have a failure story ready. The sample is small, and the reports come from different programs.