1. What is a Data Engineer at AIG?

At AIG (American International Group), the Data Engineer role is pivotal to the company's ongoing digital transformation. You are not just maintaining databases; you are reimagining how one of the world’s largest insurance organizations manages risk, processes claims, and serves millions of customers. AIG is currently bridging the gap between legacy financial systems and cutting-edge cloud-native architectures, meaning your work directly influences the speed and accuracy of critical business decisions.

As a Data Engineer here, you will likely sit within the Information Technology team or the specialized GenAI division. Your work will range from modernizing data pipelines on platforms like Snowflake and AWS to building infrastructure for Generative AI models. Whether you are developing robust ETL processes for financial reporting or architecting big data solutions for predictive modeling, your contributions ensure that data is accurate, accessible, and secure. You will collaborate closely with actuaries, data scientists, and business stakeholders, acting as the technical backbone that enables AIG to "shield the company’s systems from security risks" while driving innovation.

2. Common Interview Questions

3. Getting Ready for Your Interviews

Preparation for AIG requires a balanced approach. While technical competence is non-negotiable, AIG places a significant premium on stability, governance, and the ability to work within a regulated global enterprise. You should prepare to demonstrate that you can build systems that are not only fast but also auditable and reliable.

Technical Proficiency – 2–3 sentences describing: You will be evaluated on your core coding skills in Python and SQL, as well as your familiarity with modern data stacks (Snowflake, Spark, AWS). Interviewers look for clean, maintainable code and an understanding of how to optimize queries for large datasets typical of the insurance industry.

Domain Aptitude and Business Acumen – 2–3 sentences describing: AIG values engineers who understand why the data matters. You will be assessed on your ability to translate technical requirements into business value, specifically demonstrating an understanding of how data flows support underwriting, claims, or financial reporting.

System Design and Scalability – 2–3 sentences describing: For senior roles, you must demonstrate the ability to design end-to-end data architectures that are robust and scalable. You should be ready to discuss trade-offs between different cloud technologies (e.g., AWS SageMaker vs. Palantir Foundry) and how to handle data ingestion from diverse sources, including legacy mainframes.

Collaboration and Communication – 2–3 sentences describing: Because data engineering at AIG supports various business units—from Finance to GenAI—you must show that you can communicate complex technical concepts to non-technical partners. Interviewers will look for evidence of how you manage stakeholder expectations and work within cross-functional teams.

4. Interview Process Overview

The interview process at AIG is thorough and structured, designed to assess both your technical depth and your fit within a collaborative, corporate culture. Generally, the process begins with a recruiter screening to verify your background and interest in the specific team (e.g., Financial Data vs. GenAI). Following this, you should expect a technical screening which may involve a live coding session or a take-home assessment focused on SQL and Python data manipulation.

The final stage is typically a "Super Day" or a series of back-to-back interviews with a panel. This stage is comprehensive, involving deep dives into system design, past project experiences, and behavioral questions based on AIG’s core values. Unlike some tech-first startups, AIG’s process often emphasizes your experience with enterprise-grade tools, governance, and your ability to navigate complex organizational structures. The pace can vary, but the focus is consistently on finding candidates who are looking for a long-term career in transforming the insurance landscape.

This timeline illustrates the typical progression from the initial recruiter touchpoint through the technical validation and final onsite panel. You should use this to plan your preparation, ensuring you have refreshed your coding skills before the technical screen and prepared your project stories before the final rounds. Note that the specific technologies tested (e.g., Spark vs. SSIS) will depend heavily on whether you are interviewing for the GenAI team in Atlanta or the Financial Data team in Jersey City/Charlotte.

5. Deep Dive into Evaluation Areas

AIG’s interviews are practical and experience-based. Interviewers often pull from real-world scenarios they face, such as migrating on-premise data to the cloud or integrating third-party vendor data.

Data Warehousing & SQL Proficiency

Data is the lifeblood of insurance. You will be tested heavily on your ability to model data and retrieve it efficiently. Expect questions that go beyond simple SELECT statements; you need to demonstrate mastery of complex joins, window functions, and performance tuning.

Be ready to go over:

• Advanced SQL – Window functions (RANK, LEAD/LAG), CTEs, and optimizing slow-running queries.
• Data Modeling – Dimensional modeling (Star vs. Snowflake schemas) and how to design tables for reporting vs. transactional needs.
• Cloud Data Warehouses – Specific features of Snowflake (clustering, micro-partitions) or Oracle Exadata, depending on the team.
• Data Governance – Handling PII (Personally Identifiable Information) and ensuring data quality, which is critical in insurance.

Example questions or scenarios:

• "Write a query to find the top 3 insurance policies by premium amount for each region."
• "How would you optimize a query that joins two billion-row tables in Snowflake?"
• "Explain the difference between a Star Schema and a Snowflake Schema and when you would use each."

ETL/ELT Pipeline Design & Python

You must demonstrate the ability to move data reliably. Whether using Python scripts, SSIS, or Spark, the focus is on robustness and error handling.

Be ready to go over:

• Pipeline Orchestration – Tools like Airflow or proprietary schedulers.
• Data Transformation – Using Pandas or PySpark to clean and aggregate raw data.
• Error Handling – How you manage pipeline failures, retries, and data quality checks during ingestion.
• Legacy Integration – Strategies for extracting data from legacy systems (mainframes, on-prem databases) and moving it to the cloud.

Example questions or scenarios:

• "Design a data pipeline to ingest claims data from an external vendor API into our data lake."
• "How do you handle duplicate records arriving in a stream?"
• "Walk me through a Python script you wrote to automate a manual data process."

Big Data & Modern Architecture (GenAI/Cloud Roles)

For roles within the GenAI or Modernization teams, the bar for architectural knowledge is higher. You need to understand distributed computing and cloud-native patterns.

Be ready to go over:

• Spark/PySpark – Handling skewed data, memory management, and distributed processing concepts.
• Cloud Infrastructure – AWS core services (S3, EMR, Lambda, Glue) and containerization (Docker, Kubernetes).
• ML Ops Integration – How data engineering supports machine learning models (feature stores, model deployment pipelines).
• Advanced Concepts – CI/CD for data pipelines and Infrastructure as Code (Terraform/CloudFormation).

Example questions or scenarios:

• "How would you architect a real-time fraud detection pipeline?"
• "Explain how Spark handles shuffling and how you can minimize it."
• "Describe your experience deploying containerized applications using Kubernetes."