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Updated weekly · Last refresh Aug 30

AIG Claims Data Analyst Interview Questions

The questions to prepare for a AIG Claims Data Analyst interview. Questions from real interview reports rank first. Updated weekly.

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1
SQL & Data ManipulationStart here. 3 questions + 3 drills · ~54 min
Handling Missing Data in SQLEasy

Explain how to identify, assess, and handle missing values in SQL using NULL checks, COALESCE, and validation logic.

Data WranglingCase WhenQualityAIG Claims
Explain SQL JoinsEasy

Tests foundational SQL understanding of joining tables and combining datasets correctly.

SubqueriesJoinsData WranglingAIG Claims
Write Complex SQL QueriesMedium

Tests SQL proficiency and ability to translate business questions into correct, efficient queries.

JoinsCTEsAggregationsAIG Claims
Window Function Ranking ClaimsEasy
Practice
Practice drill

Rank open Hanover claims by amount within each active customer using RANK and a customer join.

Window FunctionsRankingpartitioningAIG ClaimsThe Hanover Insurance Group
Top Customers by Net SalesMedium
Practice
Practice drill

Compute top 10 customers by net sales using joins and aggregations, ordering by revenue with deterministic tie-breaking.

SubqueriesJoinsData WranglingGain DigitalTCSZest AI
Monthly Sales Trends by CategoryMedium
Practice
Practice drill

Aggregate monthly sales by product category and use LAG to calculate month-over-month changes.

InfrastructureToolsData WranglingTotal Wine & MoreInc.Benjamin Moore
2
Behavioral & Leadership3 questions · ~24 min
More Behavioral & Leadership questions with a free account
3
More topics5 questions · ~40 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityAIG Claims
Motivation for Data Engineering WorkEasy

Explain what drives your interest in data engineering, grounded in user needs and the value created by reliable data systems.

Jobs to Be DoneUser NeedsValue PropositionAIG Claims
Detect Fraud Patterns in ClaimsHard

Tests statistical thinking and feature/validation approaches for fraud detection in claims data.

CorrelationHypothesis TestingCausal InferenceAIG Claims
Improve Claims Process EfficiencyMedium

Tests product sense and ability to select metrics and data sources tied to operational outcomes.

Value PropositionPain PointsUse CasesAIG Claims
Optimize Slow Data ProcessingHard

Tests performance tuning skills for data pipelines and ability to diagnose bottlenecks.

InfrastructureBatch ProcessingOrchestrationAIG Claims

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The finish line: interview-readyComplete all 11 questions plus 3 hands-on drills to finish this plan.