These questions are based on real interview experiences from candidates who interviewed at this company. You can practice answering them interactively on Dataford to better prepare for your interview.

Preparation for Amazon is unique because of the company's intense focus on culture combined with technical rigor. Do not treat this like a standard interview where you can wing your behavioral answers. You need a strategy.

Leadership Principles (LPs) – Amazon evaluates every candidate against its Leadership Principles. You must understand them deeply—not just memorize the titles, but understand the nuances. Interviewers will be listening for how you demonstrate traits like Ownership, Bias for Action, and Customer Obsession in your past work.

Data Proficiency – You must demonstrate the ability to manipulate data to solve real-world problems. While the tools may vary (SQL, Excel, Quicksight, Tableau), the expectation is that you can pull your own data, clean it, and derive actionable insights without relying entirely on data engineers.

Structured Communication – Amazon values concise, structured communication. You will be expected to use the STAR method (Situation, Task, Action, Result) for all behavioral questions. Rambling answers are a red flag; clear, data-backed results are the goal.

The interview process for a Business Analyst at Amazon is rigorous and comprehensive. Based on recent candidate experiences, the process generally spans 4 to 6 weeks, though timelines can vary significantly by team and location. The process is designed to test your analytical stamina and your cultural alignment in equal measure.

Typically, you will start with an Online Assessment or a recruiter phone screen. If you pass, you move to a technical phone screen (often with a hiring manager or senior peer) that tests SQL fluency and basic behavioral fit. The final stage is the "Loop"—a series of 4 to 5 back-to-back interviews (often 45–60 minutes each). During the Loop, you will meet various stakeholders, including a "Bar Raiser"—an interviewer from a different team brought in to ensure you meet a high objective standard.

Expect a mix of technical execution (live SQL coding) and deep behavioral probing. Candidates frequently report that the process is thorough, with interviewers drilling down into the specific details of your stories to verify your contribution.

This timeline illustrates the progression from initial contact to the final decision. Use the time between the phone screen and the onsite Loop to refine your "stories" for the Leadership Principles, as this is where the bulk of the evaluation happens. Be prepared for a marathon day during the final stage; keep your energy high, as the last interview is just as important as the first.

To succeed, you need to prepare for three distinct pillars of evaluation. Amazon interviewers assign specific competencies to specific rounds, so you must be well-rounded.

Amazon Leadership Principles (Behavioral)

This is the most critical part of the interview. You will face 4–5 rounds of behavioral questions. Unlike other companies, Amazon interviewers will ask follow-up questions to ensure you truly exhibited the principles.

Be ready to go over:

• Customer Obsession: How you prioritized a customer need over a short-term business goal.
• Deliver Results: A time you hit a tight deadline despite roadblocks.
• Ownership: A situation where you stepped outside your defined role to fix a problem.
• Bias for Action: How you made a decision with incomplete data to keep a project moving.
• Earn Trust: How you handled a mistake or a conflict with a stakeholder.

Example questions or scenarios:

• "Tell me about a time you had to dive deep into a problem to find the root cause."
• "Describe a situation where you disagreed with a manager's decision. How did you handle it?"
• "Give me an example of a time you improved a process that was broken."

Technical Skills (SQL & Data Analysis)

Business Analysts at Amazon must be self-sufficient with data. You will likely face a dedicated technical round or have technical questions embedded in other rounds.

Be ready to go over:

• SQL Queries: Joins (Inner, Left, Self), Aggregations (GROUP BY, HAVING), and Window Functions (RANK, LEAD/LAG).
• Data Cleaning: Handling NULL values, casting data types, and string manipulation.
• Scenario-based Analysis: "How would you investigate a 10% drop in weekly sales?"
• Optimization: Writing efficient queries that can handle large datasets without timing out.

Example questions or scenarios:

• "Write a SQL query to find the top 3 customers by spend in each region."
• "How would you use SQL to solve a real case regarding inventory shortages?"
• "Given two tables, Orders and Customers, identify customers who haven't placed an order in the last 6 months."

Analytical Problem Solving & Case Studies

Sometimes combined with technical rounds, these questions test your business acumen. You may be given a vague business problem and asked to structure an analysis.

Be ready to go over:

• Metric Definition: Defining success metrics for a new product launch.
• Root Cause Analysis: Breaking down a high-level problem into investigate-able components.
• Trade-offs: Weighing the pros and cons of two different analytical approaches.

As a Business Analyst at Amazon, your daily work revolves around turning "noise" into "signal." You will be responsible for building and maintaining automated dashboards (using tools like Tableau or Amazon QuickSight) that leadership uses for weekly business reviews (WBRs). You aren't just refreshing spreadsheets; you are designing the logic that tracks the health of the business.

Collaboration is central to the role. You will partner closely with Product Managers, Data Engineers, and Operations Managers. Often, you will act as a translator—taking a vague request from a business leader ("Why are shipping costs up?") and converting it into a precise data query. You will then analyze the results and present them in a written narrative or a presentation, providing recommendations on how to fix the issue.

You will also drive process improvement. Amazon values Invent and Simplify. You will be expected to spot inefficiencies—such as a manual reporting process that takes 5 hours a week—and automate it using SQL and scripting, freeing up time for deeper analysis.

Candidates who secure offers typically possess a strong blend of technical execution and business savvy.

• Must-have skills:

  • Advanced SQL: Ability to write complex queries from scratch (CTEs, window functions, complex joins).
  • Data Visualization: Proficiency in Tableau, PowerBI, or Amazon QuickSight.
  • Communication: Ability to explain complex data concepts to non-technical stakeholders.
  • Educational Background: A degree in a quantitative field (Business Analytics, Economics, Math, Statistics, or Engineering) is standard.

• Nice-to-have skills:

  • Scripting: Knowledge of Python or R for more advanced statistical modeling or automation.
  • AWS Ecosystem: Familiarity with Redshift, AWS Glue, or S3.
  • ETL Experience: Basic understanding of data pipelines and how to move data between systems.