Amazon Advertising Business Intelligence Analyst interview questions & guide 2026

The questions below represent common patterns observed in Amazon Advertising interviews. While specific technical challenges will vary by team, focus on mastering the underlying concepts rather than memorizing individual queries.

Technical and Domain Expertise

These questions test your core proficiency in data manipulation and your understanding of data architecture. Expect to write clean, efficient code under time constraints.

• Write a SQL query to identify top-performing ad campaigns based on click-through rate over a specific time window.
• How would you optimize a slow-running SQL query that joins multiple large tables?

Preparation for Amazon Advertising requires a disciplined approach that balances technical mastery with a deep understanding of how to communicate your impact.

Technical Proficiency – You must be fluent in SQL and Python for data analysis. Interviewers will look for your ability to write production-ready code that is both performant and easy to maintain. Practice writing queries on complex datasets and be prepared to explain your logic while you code.

Analytical Rigor – You will be evaluated on your ability to break down ambiguous business problems into structured, analytical tasks. When faced with a case study, always start by asking clarifying questions to understand the scope and objectives before jumping into the data.

Leadership Principles – At Amazon, your behavioral answers are just as important as your technical performance. Use the STAR method (Situation, Task, Action, Result) to frame your stories. Ensure that your examples highlight your ownership, ability to dive deep, and how you deliver results.

The interview process at Amazon Advertising is rigorous and highly structured. It typically begins with an Online Assessment (OA), which serves as a technical filter focusing on SQL proficiency and basic coding. Following a successful assessment, you will likely engage in a technical phone screen where you will be expected to code live while articulating your thought process.

The final stage is the Virtual Onsite (the "Loop"), which consists of multiple back-to-back interviews. These sessions are designed to assess your technical skills, business acumen, and cultural alignment with Amazon’s values. You will likely meet with a mix of managers, peers, and a "Bar Raiser"—an interviewer from outside the immediate team tasked with ensuring a high bar for excellence across the company.

The visual timeline above illustrates the progression from initial technical screening to the multi-round loop. Candidates should interpret this as a marathon rather than a sprint; the process is designed to be exhaustive. Plan your energy accordingly, and if possible, request to split your onsite sessions across two days to ensure you remain sharp and present for every conversation.

SQL and Data Manipulation

This is the bedrock of the role. You will be evaluated on your ability to handle complex joins, window functions, and subqueries on large datasets.

• Be ready to go over:
  • Writing efficient queries for large-scale databases.
  • Handling nulls and data cleaning within SQL.
  • Performance optimization techniques for complex joins.
• Example scenarios:
  • "Write a query to calculate the rolling 30-day average for ad spend."
  • "How would you handle a situation where the data volume exceeds the memory limits of your query?"

Data Warehousing and ETL

You must demonstrate an understanding of how data flows from source to end-user.

• Be ready to go over:
  • Understanding of data pipelines and transformation logic.
  • Best practices for data modeling and schema design.
  • Troubleshooting data latency and pipeline failures.
• Example scenarios:
  • "Explain the lifecycle of a data point from the ad click to the final dashboard."
  • "How do you ensure data integrity in your ETL processes?"

Business Acumen and Visualization

Your ability to translate data into actionable insights is what differentiates a good analyst from a great one.

• Be ready to go over:
  • Selecting the right visualization for specific business metrics.
  • Identifying "the why" behind a data trend.
  • Communicating complex findings to non-technical stakeholders.
• Example scenarios:
  • "A stakeholder asks for a dashboard that tracks 50 different metrics. How do you respond?"
  • "How do you define success for a new advertising campaign?"

As a Business Intelligence Analyst, your primary responsibility is to act as the bridge between raw data and informed business action. You will spend a significant portion of your time writing and optimizing SQL queries to extract data, building robust dashboards that provide real-time visibility into campaign performance, and conducting ad-hoc deep dives into anomalous data patterns.

Collaboration is essential. You will regularly partner with Product Managers to define KPIs for new features, work with Data Engineers to improve the underlying data infrastructure, and present your findings to senior leadership to influence strategic decisions. Your success is measured by the clarity of the insights you provide and the tangible improvements in business outcomes that result from your work.

A strong candidate for this role is someone who is technically sound, intellectually curious, and capable of operating with a high degree of autonomy.

• Must-have skills:
  • Advanced proficiency in SQL (including window functions and query optimization).
  • Experience with Python for data analysis and automation.
  • Strong understanding of Data Warehousing concepts and ETL pipelines.
  • Demonstrated ability to translate business problems into analytical models.
• Nice-to-have skills:
  • Experience with cloud-based big data technologies.
  • Background in advertising technology or digital marketing metrics.
  • Proficiency in advanced data visualization tools.

Q: How difficult are the interviews? The difficulty is generally considered moderate to high. The technical rounds are straightforward but require speed and accuracy, while the behavioral rounds are intense and require deep reflection on your past work.

Q: What is the most common reason candidates fail the loop? Many candidates fail because they focus too much on the "what" (the technical solution) and not enough on the "why" (the business impact). Always tie your technical work back to how it helped the business or the customer.

Q: Should I memorize the Leadership Principles? Do not memorize them, but you must be able to map your experiences to them. When an interviewer asks a behavioral question, they are looking for specific evidence of these principles in action.

Q: How long does the process usually take? The timeline varies significantly depending on the team and location, but it is often a multi-week process. Expect at least 3–4 weeks from the initial screening to a final decision.

• Structure your answers: When answering behavioral questions, use the STAR method. It keeps your stories concise and ensures you cover the "Result" part of your experience.
• Clarify the goal: For any case study or technical problem, always ask, "What is the primary business goal?" before starting. This shows you are outcome-oriented.
• Own your mistakes: If you realize you made a mistake during a coding round, acknowledge it immediately, explain why it was wrong, and propose the correct approach. This demonstrates integrity and a growth mindset.
• Prepare your own questions: Use the "ask me any question" portion of the interview to learn about the team's current challenges. This shows genuine interest and can provide you with valuable feedback.

The Business Intelligence Analyst role at Amazon Advertising is a high-impact position that sits at the center of one of the most dynamic business units in the world. Success requires a blend of rigorous technical skill, clear communication, and a deep commitment to Amazon’s values. By focusing on your SQL proficiency, sharpening your analytical framework, and preparing concrete examples of your leadership, you can significantly improve your performance.

Candidates can explore additional interview insights, practice questions, and preparation resources on Dataford. This resource is designed to help you understand the nuances of the Amazon interview process and build the confidence necessary to succeed.

The compensation data provided above reflects the typical components of an Amazon offer, which generally includes base salary, sign-on bonuses, and restricted stock units (RSUs). Use these ranges to calibrate your expectations and understand the total compensation structure, noting that specific offers will vary based on your experience level, location, and the specific needs of the hiring team.