Candidates describe Chubb's Data Engineer interviews as two to four rounds covering SQL, Python, data modeling and, in one report, Azure and Databricks, with a manager conversation near the end.
$120K
Avg. Base Comp
$150K
Avg. Total Comp
2-4 rounds
Typical Rounds
Not reported
Process Length
Chubb Data Engineer candidates describe a process built around fundamentals, though the number of rounds varies. One candidate reports two technical rounds, one with a team member and one with the team leader, and no separate HR round. Another reports three rounds: an HR phone screen, a technical interview with the line manager, and a hiring manager round. A third describes a HackerRank assessment, two in-person technical rounds, a platform-focused round and a 30-minute manager call.
The technical content is fairly consistent. Candidates mention SQL and Python fundamentals, including joins, a query to find employees with no projects, query optimization, OLTP versus OLAP, and ETL versus ELT. One candidate was also asked about MapReduce, sorting, hashing, BFS/DFS, sliding window problems, and garbage collection in Python. Another reports data modeling (dimensional modeling, lakes versus lakehouses) and a conversational round on Azure and Databricks, covering managed identities, mounting versus volumes, cluster types and liquid clustering.
Expect to discuss your own projects in depth. One candidate said the questions felt tied to real work, and the final round included prompts like "Why Chubb?" Two of the three on-role candidates received offers. The third cleared every round but heard nothing after submitting documents, so follow up politely and keep your other applications moving.
Synthesized from 4 candidate reports by our editorial team.
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Real interview reports from people who went through the Chubb process.
The interview process for Data Engineer at Chubb was structured in three rounds: an initial HR phone screen, a technical interview with the line manager, and a final round with the hiring manager. The HR round was surprisingly intense despite being preliminary—they asked some challenging behavioral questions that felt designed to test how I'd handle pressure and navigate complex workplace scenarios. They didn't ask about salary, which was a bit unusual, but kept the conversation friendly overall. The technical round with my potential manager was where things got substantive. He clearly knew the space well and we had a genuine discussion about projects on my CV and the technologies I'd worked with. He tested my knowledge across several domains: SQL concepts like inner joins and the differences between OLTP and OLAP systems, data engineering fundamentals like ETL versus ELT pipelines, and MapReduce. There were also coding questions covering sorting, hashing, graphs with BFS/DFS traversal, sliding window problems, and tree operations. The questions felt anchored to real work rather than abstract LeetCode challenges. The final round with the hiring manager was the most pressure-focused. They were clearly assessing how I'd perform when pushed—asking questions like 'Why Chubb?' and 'Who are you in five words?' alongside technical questions like explaining garbage collection in Python. The overall vibe was professional but approachable, and the line manager's genuine engagement made it feel like a real conversation about fit rather than a box-ticking exercise.
Prep tip from this candidate
Brush up on SQL window functions, OLTP vs OLAP distinctions, and ETL/ELT pipeline concepts—those came up consistently. Be ready to discuss your CV projects in depth, not just list them, and prepare for behavioral questions that probe how you handle pressure and ambiguity.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Chubb
Select the 2nd highest salary in the engineering department
| Question | |
|---|---|
| Top Three Salaries | |
| Find Duplicate Numbers in a List | |
| Get Top N Frequent Words | |
| Covariance vs Correlation | |
| Employee Salaries | |
| Experiment Validity | |
| Random SQL Sample | |
| Rectangle Overlap | |
| Hurdles In Data Projects | |
| Size of Joins | |
| Total Spent on Products | |
| P-value to a Layman | |
| Xgboost vs Random Forest | |
| Always Excited Users | |
| WAU vs Open Rates | |
| Instagram TV Success | |
| Group Success | |
| Target Indices | |
| Classification and Regression | |
| Duplicate Rows | |
| RAG Strict Source Control | |
| Data Preparation for Imbalanced Data | |
| Transformer Encoder Layer | |
| Type I and II Errors | |
| Digitizing Student Test Scores | |
| Assumptions of Linear Regression | |
| Common Prefix | |
| Trial User Segmentation | |
| Count Transactions |
Synthesized from candidate reports. Individual experiences may vary.
One candidate reports a virtual HackerRank test of 29 questions in 90 minutes, mixing SQL basics and general computer science. Not every candidate mentions an assessment, so treat it as possible rather than guaranteed. Practicing timed SQL and CS fundamentals is a sensible way to prepare.
Candidates report technical conversations with a team member, line manager or team leader. Topics include SQL joins, OLTP versus OLAP, ETL versus ELT, basic Python, and discussion of CV projects. One candidate also reports coding questions on graphs, trees, sliding windows and hashing. Tone is described as conversational.
One candidate describes back-to-back in-person rounds on dimensional modeling, lakes versus lakehouses and query optimization. A further round covered Azure and Databricks topics such as managed identities, volumes, cluster types and parquet trade-offs. This may depend on the team's stack, so review the job description.
Two candidates describe a final conversation with a manager. One calls it a 30-minute virtual discussion about background and problem approach. Another reports pressure-style questions such as "Why Chubb?" and "Who are you in five words?", plus a Python garbage collection question.
Outcomes differ across candidates. Two on-role candidates report offers. One cleared all four rounds, was asked to submit documents and confirm a start date, then reports hearing nothing further. Timing is not consistently reported, so ask your recruiter for the expected timeline.
