AIG Data Engineer Interview Guide

AIG's data engineering loop is filtering for engineers who can operate inside a heavily regulated, legacy-adjacent environment without freezing up. Insurance at AIG's scale means data moves through compliance checkpoints, actuarial dependencies, and finance reporting chains before it reaches any analytical surface. The process is designed to surface candidates who understand data ownership in that context: who can trace lineage, defend a design decision to a risk officer, and still ship. Generic pipeline fluency is not the signal they are after. What reads as competence here is the ability to reason about correctness and auditability together, because at a company rebuilding its data infrastructure inside a regulated industry, those two concerns are inseparable. If you have worked in financial services before, that experience should come through clearly; if you have not, every technical answer should still reflect an awareness that data errors in insurance have downstream dollar consequences.

Your marginal prep hour pays most on pipeline architecture with an eye toward reliability and observability, since that is where the pipeline architecture signal concentrates. Before that, make sure your Python fundamentals are clean, because the screen will gate you out early if they are not. Spend real time on AWS data movement patterns, particularly anything touching S3 and event-driven orchestration, because those surface constantly in a shop running that stack. Skip the LeetCode-heavy prep track; there is no signal in the digest that algorithmic puzzles dominate here. The leveling context matters: the ladder has 2 levels, and L4 is mid, with a median of $145K. At L4 the bar shifts toward architectural judgment and stakeholder communication, not raw technical throughput. If you are targeting that level, every answer should carry a sentence about why the design fits the organizational context, not just the technical one.