Amazon’s massive data ecosystem gives business analysts a front-row seat to industry-shaping decisions. Whether it’s optimizing logistics or driving customer insights, the role blends analytical skills with real business impact. Business analysts at Amazon are expected to dive deep into data, identify trends, and present actionable recommendations that align with business goals. With tools like SQL, Excel, Python, and AWS at the core, analysts solve real-time challenges at scale.
The Amazon Business interview process focuses on both technical skills and business intuition. In this guide, we’ll walk through the most common Amazon Business Analyst Interview Questions to help you understand what recruiters are looking for and how to stand out in a highly competitive process.
The Business Analyst role at Amazon stands out because of the scale, complexity, and real-world impact of the problems you get to solve. As a business analyst, you’re not just reporting numbers; you’re shaping strategy. From streamlining supply chains to improving customer experiences, your insights influence Amazon’s global operations in meaningful ways.
Amazon is a place where your work is valued, your growth is supported, and your impact is recognized. The company offers competitive pay, comprehensive benefits, and tailored training programs to help you grow. With flexible work options and an inclusive culture, Amazon empowers you to build a career that aligns with your life.
The Amazon Business Analyst interview process typically moves through four stages: phone screen, technical interview, behavioral round, and the final onsite “Super Day.” Each stage is designed to assess a mix of technical expertise and cultural fit. Here’s a breakdown of what to expect at every step:
The interview process begins with a 45-minute phone or virtual interview conducted by a recruiter or hiring manager. While the focus is primarily behavioral, you should be ready for light technical questions as well. Expect to discuss your experience, how you’ve handled past challenges, and why you’re interested in the role.
The technical screening in the Amazon Business Analyst interview process typically lasts around 60 minutes. This round is designed to assess your hands-on proficiency with tools like SQL, Python, or Excel. You’ll be asked to solve real-world business problems using data, write complex queries, and possibly walk through your logic.
Behavioral interview focuses on how well you fit with the team and Amazon’s overall culture. Typically led by a hiring manager, this round dives into your past experiences to assess how you’ve demonstrated Amazon’s Leadership Principles in real scenarios. Expect questions that test your judgment, ownership, and ability to work in fast-paced or unclear situations.
In the final stage, often called Super Day, you’ll face 5 to 6 back-to-back interviews testing both your behavioral and technical skills. You’ll likely meet representatives from different teams and departments. Usually, 1 to 2 interviews focus on behavioral questions, while the remaining 3 to 4 cover technical problem-solving. Each session lasts about an hour, with short breaks in between.
Before you dive into the interviews, make sure you’re ready for both behavioral and technical questions. Study Amazon’s Leadership Principles so you can naturally integrate them into your answers. Also, brush up on your SQL and other key technical skills; these will help demonstrate your ability to think analytically and solve real business problems like a true business analyst.
Technical questions in Amazon Business Analyst interviews test your ability to work with data in real-world scenarios. You’ll encounter questions involving SQL, Python, or other analytical logic.
How can you calculate the annual cost of overlapping nightly jobs that cause downtime?
You can simulate the problem by writing a function that estimates the probability of overlap between the two jobs each night. Given that each job lasts an hour and can start randomly between 7 pm and midnight, you can simulate this scenario to find the probability of overlap. Multiply this probability by $1000 (the cost of downtime) and then by 365 (days in a year) to get the annual cost. Alternatively, you can solve this using probability by calculating the chance of overlap directly and applying the same multiplication for the annual cost.
Write a query that returns all neighborhoods that have 0 users.
To find neighborhoods with no users, you can use an SQL query that performs a left join between the neighborhoods
table and the users
table and then filters for neighborhoods where the neighborhood_id
is null.
To identify customers who placed more than three transactions each in both 2019 and 2020, you can use a SQL query that groups transactions by user and year, counts the transactions, and filters for users meeting the criteria in both years.
You need to identify users who made additional purchases on different days after their first purchase. This can be achieved by writing a SQL query that groups transactions by user_id
, orders them by created_at
, and checks for purchases made on subsequent days.
To calculate the percentage of total revenue made during the first and last years recorded in a table, you need to sum the revenue for each year and then divide the revenue of the first and last years by the total revenue. Multiply the result by 100 to get the percentage, and round it to two decimal places.
You can use a hash map to store the difference between the target and each element as you iterate through the array. For each element, check if it exists in the hash map. If it does, return the current index and the index stored in the hash map. If no such pair is found, return an empty list.
You can use the first timestamp as the starting point and create sublists for each 7-day period. For example, given the input list ['2019-01-01', '2019-01-02', '2019-01-08', '2019-02-01', '2019-02-02', '2019-02-05']
, the output would be [['2019-01-01', '2019-01-02'], ['2019-01-08'], ['2019-02-01', '2019-02-02'], ['2019-02-05']]
. This approach ensures that each sublist contains timestamps that fall within the same 7-day period starting from the first timestamp.
Given a transactions table with date timestamps, sample every 4th row ordered by the date.
You can use a SQL query that utilizes the ROW_NUMBER()
function to assign a unique sequential integer to rows within a partition of a result set, and then filter for every 4th row.
How do you find the percentage of users with at least one seven-day streak of visiting the same URL?
You need to analyze the event logs from the events
table. The task involves identifying users who have visited the same URL for seven consecutive days and then calculating the percentage of such users out of the total number of users.
You need to join the customers
and shipments
tables on customer_id
. Then, check if the ship_date
falls between membership_start_date
and membership_end_date
. If it does, set the is_member
column to ‘Y’; otherwise, set it to ‘N’.
You can write a SQL query that checks for overlapping date ranges. Specifically, for each user, you would compare their subscription’s start and end dates with those of other users to see if there is any overlap. If an overlap is found, the query should return true
(or 1
), otherwise false
(or 0
).
These topics check your ability to apply basic statistical concepts to real business scenarios. Expect questions on A/B testing, hypothesis testing, and product metrics—focused on how you draw insights and make data-driven decisions.
Logistic function maps input values to a probability between 0 and 1, while softmax is used for multi-class classification, converting input values into a probability distribution. Both are used in logistic regression to model binary or multi-class outcomes.
An unbalanced sample size in an AB test can potentially lead to bias, particularly if the smaller group does not have enough power to detect a true effect. The smaller sample size may result in higher variability and less reliable results, which could skew the interpretation of the test outcomes. To mitigate this, it’s important to ensure that both groups have sufficient sample sizes to achieve the desired statistical power.
You can perform a statistical test such as the t-test or the Mann-Whitney U test, depending on the data distribution. These tests help assess whether the observed changes are statistically significant or could have occurred by random chance.
When you run logistic regression on a dataset of perfectly linearly separable data, the algorithm will not converge because the maximum likelihood estimates for the coefficients will tend to infinity. This is known as the problem of “complete separation.”
How would you explain what a p-value is to someone who is not technical?
A p-value is a measure used in statistics to help determine the significance of your results. It tells you how likely it is that your data would occur by random chance. A low p-value (typically less than 0.05) suggests that the observed data is unlikely to have occurred by chance, indicating that the effect or difference you are testing for is statistically significant.
What is the Martingale strategy and how might it be used in online advertising?
The Martingale strategy is a betting strategy that involves doubling the bet after every loss, with the aim of recovering all previous losses and gaining a profit equal to the original stake when a win eventually occurs. In online advertising, this strategy could be applied by increasing the advertising spend after each unsuccessful campaign, with the expectation that a successful campaign will eventually offset the losses and generate a profit.
Dynamic pricing offers several benefits, including maximizing profits by adjusting prices in real-time based on supply and demand fluctuations. It allows businesses to respond quickly to market changes, optimize inventory levels, and enhance customer satisfaction by offering competitive pricing. Estimating supply and demand in this context can involve analyzing historical sales data, monitoring competitor pricing, and using predictive analytics to forecast future trends.
How would you determine the overall impact of the integration on Prime Music subscriptions?
You can analyze subscription data before and after the integration, looking for changes in subscription rates. Additionally, conducting A/B testing with a control group that does not have the integration could provide insights into its direct impact. Surveys and user feedback could also be valuable in understanding user satisfaction and reasons for subscription changes.
Behavioral or “culture fit” questions are a major part of the Amazon Business Analyst interview. These questions are designed to see how well you align with Amazon’s Leadership Principles and how you’ve handled challenges in past roles.
Why did you apply to our company?
When asked why you applied to a company, it’s important to demonstrate your knowledge of the company and align your career goals with the company’s mission and values. Highlight specific aspects of the company that appeal to you, such as its culture, products, or growth opportunities, and explain how your skills and experiences make you a good fit for the role.
It’s important to assess the urgency and importance of each task, possibly using a prioritization matrix like the Eisenhower Box. Staying organized can be achieved by using tools such as calendars, task lists, and project management software to keep track of deadlines and progress. Regularly reviewing and adjusting priorities as needed can also help maintain organization and ensure timely completion of tasks.
It’s important to maintain professionalism and focus on the issue rather than personal differences. A structured approach involves active listening, understanding the other party’s perspective, and finding common ground to reach a mutually beneficial solution. For example, if a conflict arises over project responsibilities, one might address it by organizing a meeting to discuss each party’s concerns and collaboratively agree on a fair distribution of tasks.
Highlight the actions you took that led to exceeding expectations, such as implementing innovative solutions, improving efficiency, or delivering results ahead of schedule. Explain the impact of your efforts on the project’s success and any recognition you received.
How comfortable are you presenting your insights?
Presenting complex data insights with clarity and adaptability is crucial, especially when tailored to a specific audience. Comfort in presenting insights often comes from understanding the audience’s needs and being able to adjust the presentation style accordingly.
Practice taking raw data and explaining what it means for the business. Go beyond the numbers: ask yourself, “So what?” For example, if user engagement dropped 10%, be ready to suggest 2–3 possible reasons and how you’d investigate further. Amazon loves analysts who can connect metrics to meaningful action.
Don’t just memorize the principles; apply them. Pick 2–3 that best match your working style (e.g., “Dive Deep” or “Deliver Results”), and reflect on your past projects through that lens. This makes your behavioral answers feel more authentic and tailored, something Amazon values.
Get familiar with how different Amazon businesses operate: Retail, AWS, Prime Video, and Logistics. This helps you tailor your answers with relevant examples (e.g., how you’d improve delivery times vs. subscription engagement). Bonus: It signals genuine interest in the role and company.
Amazon values how well analysts communicate with non-technical teams. Practice mock conversations where you explain data insights to a “product manager” or “stakeholder” who isn’t technical. Clear communication is often what separates a good analyst from a great one.
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The interview process typically takes around 2 to 4 weeks, depending on the role’s urgency and the team’s availability. Timelines may vary slightly based on the level of the position and how quickly each interview stage is scheduled.
Must-know tools include SQL and Excel, as they’re core to most of the day-to-day analysis. Experience with Python, Tableau, or AWS tools like Redshift and QuickSight can give you an edge. Prioritize tools that help you analyze, visualize, and clearly communicate insights from data.
Yes, Interview Query often features Amazon-specific roles with job descriptions and skill requirements.
Landing a Business Analyst role at Amazon means more than just knowing SQL or Python. It’s also about thinking like an owner, making data-driven decisions, and aligning with Amazon’s unique culture. From behavioral interviews built around Leadership Principles to hands-on technical rounds, each stage is designed to test both your analytical mindset and your ability to deliver real business impact.
Sharpen your SQL, get comfortable with tools like Excel and Python, and don’t overlook the power of clear communication. For more targeted prep, check out our blogs: SQL Business Analyst Interview Questions, Business Analyst Interview Questions, and other practice sets tailored to specific focus areas. With focused prep, you won’t just get through the interview; you’ll stand out.
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