What is a Data Analyst?

A Data Analyst at AIG turns raw data into trusted insights that guide how we underwrite, price, distribute, and service insurance products globally. You will connect business questions to data, shape how information is modeled and governed, and deliver decision-quality dashboards that executives, underwriters, claims leaders, and product managers depend on daily.

In practice, this means partnering with teams across General Insurance, the Data Office, and emerging initiatives like our Generative AI programs to design semantic models, build performant Power BI/Tableau reports, and uphold rigorous data governance. You might deliver a portfolio loss-ratio dashboard for Commercial Property, stand up row-level security for regulatory reporting, or develop a Snowflake-backed semantic model that feeds Fabric and Cognos. The work is critical, visible, and operationally impactful.

This role is compelling because it blends BI product management with hands-on development. At AIG you will own the lifecycle—from BRD to deployment—and your judgment around metrics definitions, controls, and user adoption will determine whether leaders act with confidence. Expect to influence what “good” looks like across analytics, governance, and delivery excellence.

Getting Ready for Your Interviews

Focus your preparation on three pillars: building credible, performant BI products; demonstrating rigorous data modeling and SQL capabilities; and showing you can navigate governance, security, and stakeholder alignment in a regulated environment. Bring examples that prove you can translate ambiguous business needs into stable, scalable, and trustworthy analytics solutions.

• Role-related Knowledge (Technical/Domain Skills) – Interviewers will test your fluency in Power BI/Tableau (including DAX/M or calculations), SQL, data modeling (star schema, 3NF, Data Vault), Snowflake, and BI governance (RLS, data classification, deployment pipelines). Show you can design metrics like earned vs. written premium and combined ratio and implement controls that make executives comfortable acting on results.
• Problem-Solving Ability (How you approach challenges) – You will be evaluated on how you move from BRD to solution, identify ambiguous metric definitions, triage data quality issues, and optimize dashboard/query performance. Expect to explain tradeoffs and controls when timelines, scope, or data constraints shift.
• Leadership (How you influence and mobilize others) – This role often leads without formal authority. Demonstrate how you facilitate requirements, drive Agile delivery (e.g., Rally), run design reviews, and gain consensus across Business and Technology. Show how you steward adoption through documentation, enablement, and change management.
• Culture Fit (How you work with teams and navigate ambiguity) – AIG values collaboration, inclusion, and risk-aware judgment. Highlight how you work transparently, escalate thoughtfully, document decisions, and balance speed with control. Evidence of partnering well with actuaries, underwriting, claims, or finance is a plus.

Interview Process Overview

AIG’s Data Analyst interview experience is structured yet pragmatic. You can expect a mix of conversations that explore your BI craftsmanship, data modeling rigor, and your ability to manage analytics as a product. The tone is professional and collaborative—your interviewers will be assessing not just what you know, but how you think in a risk- and control-conscious environment.

Pace varies with role seniority and team needs, but the process is designed to move decisively. Case-based prompts, a SQL or modeling exercise, and a dashboard story walkthrough are common. Expect time on governance topics (e.g., RLS, PII handling, change control) and on how you drive outcomes through Agile delivery. You’ll have opportunities to show your portfolio, articulate tradeoffs, and discuss lessons learned from complex deployments.

This visual lays out the typical sequence—from recruiter alignment to technical deep dives, case/practical work, and stakeholder panels. Use it to plan your preparation cadence: reserve time for a hands-on SQL/BI review, rehearse a 10-minute dashboard narrative, and prepare specific examples of governance decisions you’ve made. Throughout, ask clarifying questions and confirm definitions early; it demonstrates the control mindset AIG expects.

Deep Dive into Evaluation Areas

Business Intelligence & Data Visualization (Power BI/Tableau/Cognos)

This area assesses how you transform requirements into usable, performant, and secure BI products. Expect to discuss data modeling for BI, DAX/M or calculated fields, row-level security, and performance tuning strategies. You should be able to translate a BRD into a semantic model, define KPIs unambiguously, and produce an executive-ready dashboard narrative.

• Be ready to go over:
  • Data modeling for BI: Star vs. snowflake schemas, semantic layer design, grain and conformance
  • DAX/M (or calculated fields): KPI logic, time intelligence, incremental refresh strategies
  • Security & deployment: RLS/OLS, workspace strategy, deployment pipelines, usage monitoring
  • Advanced concepts (less common): Composite models, DirectQuery vs. Import vs. DirectLake (Fabric), Cognos Framework Manager, Fabric Lakehouse
• Example questions or scenarios:
  • “Design a Power BI model and dashboard for Commercial Property loss ratio by product, broker, and region. How do you define earned premium and control data access?”
  • “Your report is slow against a large Snowflake model. What steps do you take to diagnose and resolve performance?”
  • “How would you implement RLS for a broker hierarchy while preserving accurate roll-ups for regional leaders?”

SQL, Data Modeling & Warehousing

We evaluate your depth in SQL, core modeling approaches (3NF, dimensional, Data Vault), and your ability to profile and remediate data quality. Expect window functions, CTEs, joins, and reasoning about SCD Type 2 and lineage. Familiarity with Snowflake features (e.g., clustering, tasks/streams) is valuable.

• Be ready to go over:
  • SQL proficiency: Aggregations, window functions, anti-joins, performance considerations
  • Modeling tradeoffs: 3NF vs. star schema vs. Data Vault for analytics and governance
  • Data quality & lineage: Profiling, reconciliation, incident handling, auditability
  • Advanced concepts (less common): Hubs/links/satellites (Data Vault), surrogate keys, ER modeling tools, Snowflake optimization
• Example questions or scenarios:
  • “Write a SQL query to calculate 12-month rolling loss ratio by product and geography.”
  • “When would you choose Data Vault patterns over dimensional modeling at AIG?”
  • “How do you reconcile policy counts that differ between source and warehouse, and how do you document the decision trail?”

Analytics & Insurance Domain

You don’t need to be an actuary, but you should understand the core insurance analytics vocabulary and how metrics drive decisions. We’ll probe your grasp of loss ratio, expense ratio, combined ratio, frequency/severity, and how to build trustworthy KPI definitions end-to-end.

• Be ready to go over:
  • Core metrics & definitions: Earned vs. written premium, exposure, IBNR sensitivity, calendar vs. accident period
  • Operational analytics: Underwriting funnel, broker/channel performance, claims triage and cycle time
  • Decision support: Pricing, reserving insights, portfolio steering, trend analysis
  • Advanced concepts (less common): GLM basics, credibility concepts, geospatial exposure, catastrophe enrichments
• Example questions or scenarios:
  • “Explain earned vs. written premium to a non-technical stakeholder and how it affects loss ratio.”
  • “Your claims severity spikes in one region—how do you investigate and present findings?”
  • “Design a star schema to analyze policy, claim, and broker performance together.”

Data Governance, Security & Compliance

AIG operates in a heavily regulated environment. You will be assessed on data classification, PII handling, RLS/OLS, catalog/lineage practices, and how you embed governance in the SDLC. Expect practical questions about change management and audit readiness.

• Be ready to go over:
  • Access control & security: RLS/OLS, least privilege, secrets management, audit trails
  • Data governance: Definitions stewardship, quality rules, catalog/lineage, issue management
  • SDLC controls: Promotion processes, approvals, rollback plans, usage monitoring
  • Advanced concepts (less common): GDPR/CCPA applicability, SOX implications for reporting, retention policies
• Example questions or scenarios:
  • “How would you design access for regional leaders, brokers, and auditors viewing the same dashboard?”
  • “Describe how you’d document KPI definitions and lineage to support an internal audit.”
  • “A metric needs to change in production—what process ensures continuity and traceability?”

Communication, Stakeholder Management & Delivery

We measure how you elicit requirements, align on definitions, manage tradeoffs, and ship. You should demonstrate product thinking: clear scoping, prioritization, iteration, enablement, and adoption tracking—ideally using Agile practices and tools like Rally.

• Be ready to go over:
  • Requirements to roadmap: Turning BRDs into product backlogs, MVP scoping, acceptance criteria
  • Alignment & enablement: Workshops, stakeholder maps, training, documentation
  • Adoption & success: Usage analytics, feedback loops, versioning, deprecation plans
  • Advanced concepts (less common): Value realization metrics, OKRs for BI products, change management playbooks
• Example questions or scenarios:
  • “Walk us through a complex dashboard you led from BRD to deployment. How did you drive adoption?”
  • “Two executives disagree on a KPI definition—how do you reconcile and proceed?”
  • “How do you structure a 10-minute readout to senior leaders on portfolio performance?”

Use this visualization to prioritize your study plan. Larger terms typically indicate recurring focus areas—expect depth in SQL, Power BI/Fabric, data modeling, Snowflake, governance, and insurance metrics. Calibrate your examples and portfolio to reflect these themes and the kinds of scenarios AIG teams solve.

Key Responsibilities

You will own BI delivery end-to-end—shaping requirements, modeling data, building secure and performant analytics, and ensuring adoption. Day-to-day, you will partner with Business, Technology, and Data Governance to produce decision-grade dashboards and models that scale across regions and products.

• Lead the BI product lifecycle: clarify BRDs, define KPIs, model data, build reports, implement RLS/OLS, and manage deployment pipelines.
• Collaborate with underwriting, claims, finance, actuarial, and engineering to translate needs into robust solutions and well-documented definitions.
• Implement and evangelize data governance practices: classification, lineage, quality rules, and audit readiness.
• Drive user enablement and adoption: training, documentation, usage analytics, and iteration based on feedback.
• Support modernization initiatives (e.g., Snowflake, Fabric, Cognos rationalization) and contribute to enterprise ontologies where applicable.

Role Requirements & Qualifications

Strong candidates blend technical depth with product ownership and governance discipline. You should be confident moving between SQL, semantic modeling, BI development, and stakeholder conversations—always with an eye on performance, clarity, and control.

• Must-have technical skills:
  • SQL expertise (joins, window functions, performance tuning)
  • Power BI or Tableau (DAX/M or calculated fields, modeling, RLS, performance)
  • Data modeling (star schema, 3NF; SCD handling; semantic layer design)
  • Familiarity with Snowflake or modern cloud data platforms
  • Experience with BI SDLC: deployment pipelines, versioning, monitoring
• Must-have professional skills:
  • Requirements elicitation and KPI definition; storytelling with data
  • Agile delivery (e.g., Rally), backlog management, stakeholder alignment
  • Governance mindset: privacy, security, and auditability
• Nice-to-have:
  • Data Vault, Fabric/DirectLake, Cognos, Palantir Foundry
  • Python or ETL tooling; NoSQL, XML/JSON
  • Insurance or financial services domain knowledge; master/reference data
  • Exposure to AI/LLM-aware modeling or ontology design
• Typical experience:
  • ~5+ years in BI development and project delivery with client-facing presentations
  • Demonstrated leadership of complex, multi-team analytics initiatives

This reflects recent compensation insights for comparable Data Analyst/Data Modeler roles surfaced from Dataford’s dataset. Actual compensation at AIG varies by location, seniority, and scope (e.g., platform leadership, governance responsibilities, toolset breadth). Discuss total rewards holistically—base, bonus, benefits, and on-site expectations—when aligning on level and offer.

Common Interview Questions

Expect questions across technical depth, BI delivery, governance, and leadership. Prepare concise stories with outcomes, metrics, and lessons learned. Where possible, tie examples to insurance-relevant KPIs and governance decisions.

Technical / BI Development

You will be asked to demonstrate practical BI craftsmanship and model design choices.

• How do you structure a semantic model in Power BI for policy, claim, and broker analysis, and why?
• Explain a complex DAX measure you wrote (e.g., 12M rolling loss ratio) and how you validated it.
• What steps do you take to optimize a slow dashboard connected to Snowflake?
• When would you choose Import vs. DirectQuery vs. DirectLake, and what controls change?
• How do you implement and test RLS for multi-tenant access while preserving correct roll-ups?

SQL & Data Modeling

Your interviewer will test your fluency in SQL and your judgment in modeling for analytics.

• Write a SQL query to compute combined ratio by quarter with a windowed prior-period comparison.
• Compare 3NF, dimensional, and Data Vault for analytics at enterprise scale.
• How do you manage SCD Type 2 in a star schema and expose it cleanly to BI tools?
• Describe your approach to data profiling and reconciling conflicting counts across sources.
• How would you design lineage and documentation to support audit readiness?

Analytics & Insurance Domain

Demonstrate comfort with core insurance metrics and operational analytics.

• Define earned vs. written premium and why it matters for performance reporting.
• How do you diagnose a spike in severity in one region and present it to leadership?
• What KPIs would you propose for broker/channel performance and why?
• How would you structure a dashboard to track claim cycle time and leakage?
• Describe an analysis that influenced pricing or portfolio steering.

Governance, Security & Risk

Show how you embed control and compliance in analytics delivery.

• Walk us through your approach to classifying data and handling PII in BI.
• How do you design a change-management process for KPI definitions in production?
• What is your strategy for secrets, credentials, and audit trails in BI environments?
• Describe how you would set up access for auditors vs. business leaders.
• Share a time you identified a governance gap—how did you resolve it?

Behavioral / Leadership & Delivery

We’ll explore how you lead through influence and ship reliably.

• Tell us about a time you aligned stakeholders with competing KPI definitions.
• Describe a challenging BRD you converted into a successful MVP—what changed?
• How do you use Agile practices (e.g., Rally) to manage BI backlogs and releases?
• Share an example of enabling adoption through documentation and training.
• How do you communicate risks and tradeoffs to senior leaders?

Use this module to practice interactively on Dataford—filter by topic to build reps where you need them most. Rehearse aloud and time your responses; aim for crisp, outcome-focused stories that highlight your decisions, controls, and impact.

Frequently Asked Questions

Q: How difficult is the interview, and how much time should I allocate to prepare?
Expect a moderate-to-high level of rigor focused on practical BI and governance. Plan 2–3 weeks to refresh SQL, DAX/calculations, data modeling, and a 10-minute dashboard narrative with clear KPI definitions.

Q: What makes successful candidates stand out at AIG?
Clear, tested KPI definitions; strong BI performance/scalability; and a visible governance mindset. Candidates who can lead from BRD to deployment—and prove adoption and control—stand out.

Q: What’s the culture like on data teams?
Collaborative, professional, and outcomes-oriented, with a strong emphasis on inclusion and risk-aware decision-making. You’ll work closely with both Business and IT to balance speed with control.

Q: What is the typical timeline and next steps after interviews?
Timelines vary by role, but many processes complete within 2–4 weeks. Use recruiter touchpoints to clarify level, scope, location expectations, and any follow-up materials or references.

Q: Is the role remote?
Many teams prioritize in-person collaboration; specifics depend on team and location. Confirm expectations with your recruiter early in the process.

Other General Tips

• Open with definitions: Before building solutions live, confirm KPI definitions, grain, and access needs. It signals control and prevents rework.
• Bring a sanitized portfolio: One or two concise Power BI/Tableau examples with synthetic data and a short narrative create immediate credibility.
• Show your governance muscle: Proactively mention RLS, lineage, change control, and audit readiness in your walkthroughs.
• Prove performance: Describe real steps you took to reduce refresh time, improve query plans, or optimize models.
• Use business language: Tie your technical choices to business outcomes—portfolio steering, pricing confidence, claim efficiency.
• Quantify impact: Adoption rates, SLA improvements, defect reductions, and time-to-insight make your results tangible.

Summary & Next Steps

A Data Analyst at AIG shapes how a global insurer understands and manages risk—by delivering trusted, governed, and actionable analytics. You will lead BI solutions from BRD to production, model the data that matters, and embed controls that earn executive confidence.

Center your preparation on five areas: BI development, SQL/modeling, insurance metrics, governance/security, and communication/delivery. Prepare a crisp dashboard narrative, rehearse core insurance KPIs, and curate 2–3 examples where your decisions improved reliability, scalability, or adoption.

Approach this process with confidence. You are interviewing for a role that blends craft and leadership—and your preparation will show. Explore additional practice and insights on Dataford, refine your stories, and be ready to demonstrate how you move organizations from data to judgment with clarity and control.