Swiss Re Data Engineer Interview Guide

Our candidates report a clear mismatch between the posted stack and what Swiss Re actually probes. Even when the role is framed around PySpark, Python, Azure Databricks, and Azure Data Factory, the interview signal that keeps showing up is deep PL/SQL fluency, especially around stored procedures. That tells us the team is not just screening for modern cloud tooling; they want someone who can work comfortably in a more traditional enterprise data environment where SQL logic still carries a lot of the weight.

A recurring theme is that the company seems to value practical database craftsmanship over buzzword alignment. In the experience we reviewed, the interviewer kept drilling into SQL despite SQL being described as a nice-to-have in the job description. That kind of mismatch is important: it suggests candidates who only prepare for the advertised Azure/Spark stack may feel blindsided, while those who can speak to relational design, procedural SQL, and how data moves through legacy systems are better positioned. We’ve seen this pattern before in insurance, where platform modernization often coexists with older production dependencies.

The other non-obvious factor is process fatigue. One candidate described a long delay before the interview even began, and by the time the technical discussion started, the energy was already gone. That matters because Swiss Re appears to test for composure as much as knowledge. The candidates who do best here are usually the ones who can stay crisp and specific even when the format feels less polished than expected.