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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 daily.

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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
Write Complex SQL QueriesMedium

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

JoinsCTEsAggregationsAIG Claims
Explain SQL JoinsEasy

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

SubqueriesJoinsData WranglingAIG Claims
Handling Missing DataMedium
Practice
Recently asked
Practice drill

Standardize missing observation fields with fallback values, missingness flags, and ordered output.

Window Functionsmissing valuesdata cleaningEversource EnergyCincinnati Children's HospitalAdtalem Global Education
Aggregate Query in SQLMedium
Practice
Practice drill

Summarize AIG Claims records by status using counts and total claim amounts.

null handlingJoinsGroup ByAIG Claims
Average Claim Amount by MonthEasy
Practice
Recently asked
Practice drill

Calculate the average claim amount for each calendar month using PostgreSQL date formatting and aggregation.

Date FunctionsGroup ByAggregationsAIG Claims
2
Behavioral & Leadership3 questions · ~24 min
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3
More topics5 questions · ~40 min
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
Data Quality in ETL PipelinesEasy
Recently asked

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityAIG 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.