Swiss Re Data Analyst interview typically runs 2 rounds: pre-recorded video interview and live conversation. It usually takes about 1-2 weeks and is fairly structured, with a standout pre-recorded step.
$97K
Avg. Base Comp
$150K
Avg. Total Comp
3-4
Typical Rounds
2-4 weeks
Process Length
We've seen Swiss Re lean toward a very grounded definition of data analyst readiness: can you explain your background clearly, and do you actually know the everyday tools well enough to use them without hesitation? In the candidate experience we reviewed, the questions stayed close to the basics — SQL, Power BI, Python, and Excel — which tells us the bar is less about clever tricks and more about solid working familiarity with the analyst stack. That matters here because the process seems designed to separate people who have touched the tools from people who can reliably use them in a business setting.
A recurring theme is that Swiss Re does not appear to reward overcomplication. The SQL exercise was described as simple, involving a query between two views, and the overall tone was practical rather than algorithmic. We've also noticed that the video format can feel awkward because it removes the natural back-and-forth, so candidates who sound polished only in conversation may struggle to come across as coherent on camera. What seems to make the difference is being able to speak naturally about your experience while showing comfort with core analyst workflows. In other words, this is a process where clarity, calm delivery, and everyday technical fluency carry more weight than flashy depth.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Swiss Re process.
The process felt fairly structured overall, but the part that stood out most to me was the pre-recorded video interview. I had to submit a video response to pre-made questions, which was a little awkward because there wasn’t any real back-and-forth, and I honestly would have preferred an in-person conversation. That said, the questions themselves were straightforward and focused on the basics of the role. I was asked to introduce myself, and then there were simple questions around SQL, Power BI, Python, and Excel, so it was really testing whether I had a solid working understanding of the core tools a data analyst uses day to day.
There was also a small SQL task that asked me to perform a query between two views, and it was pretty simple rather than deeply technical. The overall vibe was more about communication and practical familiarity than hard algorithmic problem-solving. In the live conversation portion, when I did get to speak with someone, it felt pleasant and informal, almost like a normal discussion rather than a stressful interview. I ended up receiving the offer, and my main takeaway was that it helps to be ready to explain your experience clearly and to refresh the fundamentals in SQL, Power BI, Python, and Excel before going in. If you’re preparing for this process, I’d focus less on advanced coding and more on being comfortable with the everyday analyst stack and talking through your background confidently.
Prep tip from this candidate
Refresh the basics of SQL, Power BI, Python, and Excel, and be ready for a simple SQL query task involving two views. Also practice a concise self-introduction, since that came up directly.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Swiss Re
Explain what a p-value is to someone who is not technical
| Question | |
|---|---|
| 2nd Highest Salary | |
| Employee Salaries | |
| Top Three Salaries | |
| Experiment Validity | |
| Bagging vs Boosting | |
| Random SQL Sample | |
| Hurdles In Data Projects | |
| Booking Regression | |
| Total Spent on Products | |
| Size of Joins | |
| Delivery Estimate Model | |
| Instagram TV Success | |
| Group Success | |
| Covariance vs Correlation | |
| Get Top N Frequent Words | |
| Fair Coin | |
| Lasso vs Ridge | |
| Assumptions of Linear Regression | |
| Always Excited Users | |
| WAU vs Open Rates | |
| Three Zebras | |
| Find Duplicate Numbers in a List | |
| Recruiting Leads | |
| Target Indices | |
| Success Measurement | |
| Classification and Regression | |
| Duplicate Rows | |
| Data Preparation for Imbalanced Data | |
| Type I and II Errors |
Synthesized from candidate reports. Individual experiences may vary.
The process begins with an initial screening to confirm basic fit for the Data Analyst role. This stage appears to focus on your background, communication skills, and overall experience before moving into the more structured assessments.
Candidates submit video responses to pre-made questions rather than speaking live with an interviewer. The questions are straightforward and cover introductions plus core analyst tools like SQL, Power BI, Python, and Excel, with an emphasis on practical familiarity over advanced technical depth.
There is a small SQL task involving a query between two views. The exercise is described as simple and is meant to check day-to-day SQL competency rather than algorithmic problem-solving.
A live discussion follows, and it feels informal and conversational rather than highly stressful. This round seems to assess how clearly you can explain your experience and how comfortably you can discuss your work with the team.