AIG Data Analyst interview typically runs 5 rounds: HR screen, behavioral rounds, technical rounds, assessment, and an onsite-style interview. The process usually takes 1-2 months and is notably drawn out with varied rounds.
$71K
Avg. Base Comp
$120K
Avg. Total Comp
5
Typical Rounds
1-2 months
Process Length
Our candidates report that AIG’s data analyst interviews are less about proving advanced analytics chops and more about showing you can operate cleanly in a structured, business-facing environment. A recurring theme is resume alignment: interviewers repeatedly circle back to past projects, what you owned, and how directly that experience maps to the role. Even the technical conversations seem designed to check whether you can explain your thinking clearly, not whether you can solve unusually hard problems under pressure.
We’ve also seen that the bar is surprisingly uneven across conversations. One candidate described a couple of easy LeetCode-style questions, while another had a verbal SQL discussion with no live coding at all. That tells us AIG is likely screening for basic SQL fluency and communication under ambiguity more than for deep algorithmic skill. The awkwardness of an off-camera interviewer came up too, which suggests candidates should be ready for a process that can feel formal and a bit detached; being concise, steady, and easy to follow matters more than trying to overperform.
The other pattern that stands out is how generic some of the later conversations felt. Multiple rounds leaned on standard behavioral prompts and strengths-style questions, which means the real differentiator is often whether your examples sound credible, specific, and relevant to the work. In our view, AIG is looking for someone who can translate experience into business context without overselling it. Candidates who do best here tend to sound practical, polished, and comfortable discussing SQL and past impact in plain language.
Synthesized from 1 candidate report by our editorial team.
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Topics based on recent interview experiences.
| Question | |
|---|---|
| 2nd Highest Salary | |
| Employee Salaries | |
| Random SQL Sample | |
| Bagging vs Boosting | |
| Booking Regression | |
| P-value to a Layman | |
| Hurdles In Data Projects | |
| Size of Joins | |
| Random Forest Explanation | |
| Scalped Ticket | |
| WAU vs Open Rates | |
| Missing Housing Data | |
| Three Zebras | |
| Assumptions of Linear Regression | |
| Target Indices | |
| Classification and Regression | |
| Success Measurement | |
| RAG Strict Source Control | |
| Data Preparation for Imbalanced Data | |
| Duplicate Rows | |
| Netflix Churn Prediction | |
| Modifying a Billion Rows | |
| Type I and II Errors | |
| Second Ace | |
| Swap Variables | |
| Overfit Avoidance | |
| Poker Pair | |
| The Longest Journey | |
| Why Do We Need Time Series Models? |
Synthesized from candidate reports. Individual experiences may vary.
The process begins with an HR conversation focused on your background, resume, and how your past work relates to the Data Analyst role. This stage is used to assess basic fit and communication skills.
Candidates go through several behavioral interviews with different leads. These rounds are standard and center on resume details, past projects, and overall fit for the team.
The technical portion is relatively light and may include easy LeetCode-style questions in one interview. Another technical round consists of verbal SQL questions rather than live coding, so explaining your reasoning clearly is important.
An assessment is included in the process before the final interview stage. The experience suggests it is part of evaluating baseline technical comfort and readiness for the role.
The process ends with a 30-minute interview with two team members. This round is mostly behavioral, with generic questions about strengths and other standard fit prompts rather than deep technical evaluation.