Amazon DSP Business Intelligence Analyst interview questions & guide 2026

The questions below represent common themes identified in recent interview experiences. While exact questions vary by team and interviewer, you should focus on mastering the underlying concepts and preparing structured, data-driven responses.

SQL and Data Manipulation

These questions test your ability to write efficient, complex queries to extract insights from large datasets. Expect to be asked to write code in a live environment.

• Write a query to calculate the rolling average of ad spend over the last 30 days.
• How would you optimize a query that is performing slowly on a large table?

Preparation at Amazon DSP requires a disciplined approach. You must be able to demonstrate both deep technical expertise and a clear understanding of the business impact of your work.

Technical Proficiency – You must be fluent in SQL and comfortable with data analysis using Python. Interviewers evaluate your ability to write clean, performant code under pressure, so practice writing queries without IDE assistance.

Problem-Solving Structure – When faced with an ambiguous case study, never jump straight to the solution. Clearly state your assumptions, define the metrics you would use to measure success, and outline a logical step-by-step approach before diving into the data.

Leadership Principles – Your behavioral answers are as important as your technical ones. Use the STAR method (Situation, Task, Action, Result) to structure your stories, ensuring you highlight your personal contribution and the measurable impact you delivered.

Business Acumen – You must demonstrate that you understand the "why" behind the data. Strong candidates connect their technical findings to broader business goals like revenue growth, operational efficiency, or customer experience.

The interview process at Amazon DSP is rigorous and highly structured. It typically begins with an online assessment or a technical phone screen, followed by a series of rounds that include both deep-dive technical assessments and behavioral interviews. You should expect a high degree of consistency, as interviewers are trained to evaluate candidates against specific, predefined criteria.

The visual timeline above illustrates the standard progression from initial assessment to the final "loop." You should interpret this as a marathon rather than a sprint. Pace your preparation by ensuring you have enough time to review both your technical fundamentals and your behavioral stories, rather than cramming for one at the expense of the other.

SQL and Data Engineering

This is the core of the role. You are evaluated on your ability to write accurate, performant SQL and your understanding of data warehousing concepts.

• Data Modeling – Designing schemas that are efficient for reporting.
• Query Optimization – Understanding execution plans and indexing.
• ETL Concepts – How data moves from source to warehouse and how to handle failures.
• Advanced concepts – Window functions, common table expressions (CTEs), and query performance tuning.

Data Analysis and Visualization

This area tests your ability to turn data into a narrative that stakeholders can act upon.

• Dashboard Design – Choosing the right visualizations to highlight key business metrics.
• Communication – Explaining complex findings to a non-technical audience.
• Metric Definition – How to define and track performance indicators accurately.

Behavioral and Leadership

Amazon heavily weights these rounds to ensure you will thrive in their unique culture.

• Ownership – Going beyond the job description to solve problems.
• Customer Obsession – Ensuring your work solves a real problem for the end user.
• Bias for Action – Making decisions with the data available rather than waiting for perfection.

As a Business Intelligence Analyst, your primary focus is to serve as the bridge between raw data and strategic business decisions. You will spend a significant portion of your day writing and maintaining SQL queries to support ongoing reporting needs and ad-hoc analysis. You will be expected to build and maintain high-impact dashboards that provide real-time visibility into the performance of the Amazon DSP platform.

Collaboration is central to your daily workflow. You will work closely with Data Engineers to ensure data pipelines are robust and with Product Managers to define the success metrics for new features. When a product launch occurs or an anomaly is detected, you are the one who digs into the logs to provide the explanation. You are expected to be an active participant in team meetings, offering data-backed perspectives that steer the direction of the business.

A competitive candidate for this role will demonstrate a high level of proficiency in data manipulation and a clear understanding of the digital advertising ecosystem.

• Must-have skills – Expert-level SQL, proficiency in Python for data analysis, experience with data visualization tools (e.g., Tableau, QuickSight), and a strong background in data warehousing.
• Experience level – A proven track record of delivering data-driven projects in a fast-paced environment. Prior experience in advertising technology or high-scale distributed systems is highly valued.
• Soft skills – Exceptional communication skills, the ability to manage stakeholder expectations, and a proactive approach to identifying and solving problems before they escalate.
• Nice-to-have skills – Familiarity with cloud-based data platforms, experience with machine learning metrics, and a solid understanding of A/B testing methodologies.

Q: How long does the interview process typically take? The process can be time-consuming, often taking several weeks from the initial screen to the final decision. Be prepared for some variation in communication speed, but rest assured that the process is highly structured once it begins.

Q: Should I focus more on technical or behavioral preparation? You should treat them with equal importance. A perfect technical performance can be offset by a poor behavioral showing, so dedicate significant time to both.

Q: What is the most common reason candidates fail the technical round? The most common mistake is jumping into the analysis or coding phase without first asking clarifying questions. Always define the problem and your approach before writing a single line of code.

Q: Can I use multiple days for the virtual onsite? Yes, if given the choice, it is often better to split the interview loop over two days. This allows you to stay sharp and maintain your energy levels throughout the intensive 5-hour loop.

• Use the "Ask Me Anything" time – At the end of every round, use this time to gain insights into what the interviewer valued. Ask directly what they are looking for in a candidate and how you performed against those expectations.
• Master your resume projects – Be prepared to go into extreme detail on any project listed on your resume. You should be able to explain the "why," the "how," and the specific impact of your work.
• Focus on the "Step Back" – If you are asked a question about how you would react to a crisis or an allegation, do not immediately jump to your technical approach. First, demonstrate that you would step back to verify the facts and understand the context.
• Avoid over-complication – Keep your answers focused and your code clean. Complex, unreadable solutions are rarely preferred over simple, efficient ones.

The Business Intelligence Analyst role at Amazon DSP offers a unique opportunity to work at the forefront of advertising technology. Success requires a rare combination of technical rigor and business intuition. By focusing your preparation on SQL performance, structured problem-solving, and the core Leadership Principles, you can significantly improve your chances of success.

You can explore additional interview insights, practice questions, and preparation resources on Dataford. Stay focused, be methodical in your approach, and remember that every interview is an opportunity to showcase your ability to drive impact through data.

The compensation data above provides a range of potential earnings for this role. Candidates should interpret these figures as a guide, keeping in mind that total compensation at Amazon DSP often includes base salary, stock options, and performance-based bonuses, which can vary significantly based on your level of experience and location.