AIG Data Engineer interview questions & guide 2026

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.

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.

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."

As a Data Engineer at AIG, your day-to-day work is a blend of building new capabilities and ensuring the stability of critical financial data. You will spend a significant portion of your time designing and developing data pipelines that ingest vast amounts of structured and unstructured data. This involves writing production-quality code in Python and SQL, and managing workflows in environments like Snowflake, AWS, or Oracle.

Collaboration is central to the role. You will partner with Data Scientists to operationalize Machine Learning models, ensuring they have the clean, reliable data required for training and inference. Simultaneously, you will work with business analysts and actuaries to understand their reporting needs, often translating vague business requirements into concrete technical specifications. For senior roles, you will also be responsible for mentoring junior engineers, managing vendor relationships, and driving architectural decisions that align with AIG’s long-term cloud strategy.

Successful candidates at AIG combine strong technical engineering skills with the professional maturity required for a global financial institution.

Must-have skills:

• Core Engineering: strong proficiency in Python and advanced SQL is mandatory.
• Data Platforms: Experience with cloud data warehouses (Snowflake is highly preferred) or big data processing frameworks (Apache Spark/PySpark).
• Pipeline Development: Proven ability to build and maintain robust ETL/ELT pipelines using tools like SSIS, Airflow, or AWS Glue.
• Cloud Experience: Hands-on experience with cloud ecosystems, specifically AWS (S3, EMR, Lambda).

Nice-to-have skills:

• Domain Knowledge: Prior experience in Property & Casualty (P&C) insurance or the financial services sector is a significant differentiator.
• GenAI/ML Ops: Experience with platforms like Palantir Foundry, AWS SageMaker, or deploying LLMs.
• Legacy Systems: Familiarity with Oracle Exadata or mainframe modernization projects.
• Containerization: Proficiency with Docker and Kubernetes for deploying data applications.

Q: How technical are the interviews compared to big tech companies? AIG’s interviews are technically rigorous but tend to be more practical than theoretical. You are less likely to face obscure dynamic programming puzzles and more likely to face questions about data modeling, SQL optimization, and real-world pipeline design.

Q: What is the work culture like for Data Engineers? The culture is collaborative and professional. AIG values "in-person collaboration," so expect a hybrid model with a strong emphasis on being in the office. The environment is supportive, with a focus on long-term stability and quality over "move fast and break things."

Q: Do I need insurance industry experience to be hired? While not strictly required for all roles, it is a huge plus. If you don't have it, you must demonstrate a strong willingness to learn the domain (premiums, claims, risk) quickly. For senior roles, financial data experience is often expected.

Q: What is the typical timeline for the interview process? The process can be slightly slower than in pure tech sectors due to the size of the organization and the number of stakeholders involved. It typically takes 3 to 6 weeks from the initial screen to an offer.

Q: Is the role focused on legacy maintenance or new development? It is a mix. AIG is heavily investing in modernization (GenAI, Cloud), but you will inevitably interact with legacy systems. The "GenAI" roles in Atlanta are more forward-looking, while other roles may involve more migration and stability work.

Understand the "Why": When answering technical questions, always tie your solution back to business impact. For AIG, data isn't just numbers; it's risk management. Explain how your data pipeline ensures regulatory compliance or helps underwriters make better decisions.

Brush up on Data Governance: AIG is a regulated financial entity. Mentioning concepts like data lineage, security (PII/GDPR), and auditability in your system design answers will set you apart from candidates who only focus on speed.

Ask Intelligent Questions: In your interviews, ask about the specific tech stack of the team. "Are you currently using Snowflake or transitioning to it?" or "How does the data engineering team collaborate with the actuaries?" creates a great impression.

A role as a Data Engineer at AIG offers a unique opportunity to apply modern engineering practices to complex, high-impact problems in the financial sector. Whether you are building the foundations for Generative AI or streamlining critical financial reporting, your work will directly support the company's mission to help the world manage risk. This is a place where stability meets innovation, offering a career path that is both technically challenging and professionally rewarding.

To succeed, focus your preparation on the fundamentals: advanced SQL, Python scripting, and solid ETL design principles. Combine this with a clear demonstration of how you collaborate with business partners and manage data quality. Approach the process with confidence—your ability to build reliable data systems is exactly what AIG needs to drive its transformation.

The salary data provided gives you a baseline for the Assistant VP level and similar senior engineering roles. Keep in mind that AIG’s total compensation package often includes a performance-based bonus and a comprehensive benefits package ("Total Rewards Program"). Compensation can vary significantly based on location (e.g., Jersey City vs. Charlotte) and the specific technical demands of the role.

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