Amazon DSP Business Intelligence Analyst interview questions & guide 2026

The following questions reflect the patterns observed in recent interview experiences. While exact questions will vary by team and interviewer, focus on mastering the underlying concepts rather than rote memorization.

Technical & Domain Expertise

These questions assess your core proficiency in data manipulation and your understanding of data warehousing and ETL architecture.

• Write a complex SQL query involving multiple joins and window functions to calculate year-over-year growth.
• How would you optimize a slow-running SQL query on a massive table?

Preparation for Amazon DSP requires a balanced approach. You must demonstrate high technical fluency, an ability to navigate ambiguity, and a deep alignment with Amazon’s culture.

Technical Proficiency – You must be fluent in SQL and proficient in Python for data analysis. Interviewers expect you to write clean, efficient code on the fly and explain your logic as you go. Practice complex queries, window functions, and data transformation techniques.

Analytical Rigor – You will be evaluated on your ability to break down large, ambiguous problems into smaller, manageable components. Focus on "stepping back" before diving into code; ensure you understand the business context and the "why" behind the analysis before you start building.

Leadership & Culture – Amazon’s Leadership Principles are non-negotiable. You must be able to map your past experiences to these principles. Do not just list your successes; focus on the specific actions you took, the decisions you made, and the measurable impact you achieved.

Communication – You must be able to translate technical insights into business narratives. Practice explaining your data findings to a hypothetical non-technical stakeholder to ensure you can communicate "so what" clearly and concisely.

The interview process at Amazon DSP is rigorous, structured, and highly consistent. You should expect a multi-stage funnel that begins with an online assessment or technical screening and culminates in a multi-round "loop" or virtual onsite. The process is designed to test your technical skills, your analytical problem-solving capability, and your cultural fit against the Leadership Principles.

The pacing can be deliberate, with significant time between stages, but the interview days themselves are intense. You will likely meet with a mix of managers, peers, and a "Bar Raiser"—an interviewer from a different team tasked with ensuring a high and consistent hiring bar across the company.

The visual timeline above illustrates the progression from initial screening to the final onsite loop. Use this to pace your preparation; treat the early stages as a test of your technical fundamentals and the onsite loop as a test of your behavioral consistency and depth of experience.

SQL & Data Engineering

This is the foundational skill for all Business Intelligence Analyst roles. You are expected to demonstrate expert-level knowledge of SQL syntax and data architecture.

Be ready to go over:

• Advanced SQL (window functions, CTEs, complex joins).
• ETL process design and data modeling.

As a Business Intelligence Analyst, your primary responsibility is to act as a bridge between raw data and strategic decision-making. You will spend a significant portion of your time maintaining and improving existing data pipelines, ensuring the integrity and availability of data that fuels Amazon DSP campaigns. This involves writing robust SQL queries and maintaining Python scripts that automate data extraction and transformation.

Beyond maintenance, you will drive analytical projects that address specific business pain points. This includes building intuitive dashboards that allow product managers and account executives to monitor performance in real-time. You will often work in a cross-functional capacity, collaborating with data engineers to improve data infrastructure and with product teams to define the success metrics for new advertising initiatives.

To be competitive for this role, you must demonstrate a mix of deep technical skill and strong business intuition.

• Must-have skills:
  • Advanced SQL proficiency (required for all technical screens).
  • Experience in data warehousing and ETL architecture.
  • Strong Python skills for data analysis and automation.
  • Experience building and maintaining data visualizations/dashboards.
• Nice-to-have skills:
  • Knowledge of the digital advertising ecosystem (e.g., DSP, SSP, ad exchanges).
  • Familiarity with cloud-based big data technologies.
  • Experience with statistical modeling or machine learning concepts.

Q: How long should I spend preparing for the interviews? A: Most successful candidates dedicate at least 3–4 weeks of focused preparation. This allows enough time to refresh your SQL coding skills, build STAR-based stories for behavioral rounds, and research the Leadership Principles.

Q: What is the "Bar Raiser" and why does it matter? A: The Bar Raiser is a specially trained interviewer from outside the hiring team who evaluates candidates against a high, company-wide standard. They have veto power, so ensure your answers are consistent, thoughtful, and demonstrate strong leadership potential throughout the entire loop.

Q: How technical are the behavioral rounds? A: Even in behavioral rounds, you should expect to discuss the "how" of your past projects. Be prepared to explain the technical challenges you faced, why you chose a specific approach, and what the technical trade-offs were.

Q: Can I use notes during the virtual onsite? A: While you should not read from a script, having a list of your STAR stories and key projects on a second screen or paper is common practice. It helps ensure you cover all necessary points without rambling.

• Master the STAR method: Every behavioral answer must follow this structure. It keeps your response focused and ensures you highlight your specific actions and the measurable results.
• Ask clarifying questions: In technical and case rounds, never assume the requirements. Ask questions to define the scope, the data constraints, and the business goal before writing a single line of code.
• Prepare for the "Why Amazon?" question: Understand the Amazon business model and why you want to work on DSP specifically. Show genuine interest in the scale and complexity of the platform.
• Debrief your own performance: After every round, take notes on what you felt went well and what didn't. This helps you refine your stories for the next interviewer.

The Business Intelligence Analyst role at Amazon DSP is an opportunity to work at the cutting edge of advertising technology. Success requires a rare combination of deep technical expertise and the ability to think critically about complex business problems. By focusing your preparation on SQL mastery, Leadership Principle alignment, and structured analytical thinking, you will be well-positioned to succeed.

You can explore additional interview insights, practice questions, and preparation resources on Dataford to sharpen your skills. Remember that every interview is a chance to demonstrate your potential, and with dedicated practice, you can navigate the rigor of the Amazon process with confidence.

The compensation data provided reflects market trends for Business Intelligence Analyst roles at this level. When interpreting these figures, consider the total compensation package, which typically includes base salary, sign-on bonuses, and Amazon restricted stock units (RSUs). These components are designed to be competitive and reflect your level of experience, geographic location, and the specific requirements of the team you are joining.