Amazon business intelligence engineer interview questions

Amazon business intelligence engineer interview questions
Company – Amazon
Designation – Business Intelligence Engineer
Experience required – 1 to 5 years
Topics to prepare – SQL, Project related, Amazon 14 Principles
Salary range – 24 to 48 LPA (Our student got an offer of 22 LPA Base + 5 Lakhs joining bonus for 2 years + $50,000 stocks for 4 years – 5%,15%,40%,40%)

Amazon business intelligence engineer interview questions

Amazon BIE Round details – Amazon business intelligence engineer interview questions

1. Phone Screening (1-2 rounds)

First Phone Screen (HR Screening): This round focuses on understanding your background, work experience, and alignment with Amazon’s Leadership Principles. Basic questions about your experience with data analysis, SQL, and BI tools might be asked.
Technical Phone Interview: This can include a mix of SQL, data modeling, and problem-solving questions. You may be asked to write SQL queries or solve simple technical problems.

2. Onsite/Virtual Interviews (4-5 rounds)

The onsite or virtual interview is typically divided into 4 or 5 distinct rounds, each focusing on different aspects:

Technical Round (SQL and Data Analysis): Expect deep-dive questions on SQL queries, data transformations, data cleaning, and ETL processes. You may be asked to write and optimize complex SQL queries.
Data Modeling: You will be asked to design a data model or schema based on a given business scenario. This round tests your ability to handle large datasets, create relationships between tables, and ensure data accuracy.
Problem-Solving & Analytical Thinking: This round will focus on your ability to think critically and solve real-world business problems using data. The interviewer might present a case study, asking how you would approach it with the available data.
Behavioral Interview (Leadership Principles): A significant portion of the interview is dedicated to assessing how well you align with Amazon’s Leadership Principles. You will be asked to share examples from your past experience where you demonstrated principles like Customer Obsession, Ownership, and Delivering Results. The STAR (Situation, Task, Action, Result) method is commonly used for answering these questions.
Business Acumen: Amazon values BIE candidates who understand not only data but also the business implications of data insights. In this round, you may be asked how you would use data to drive business decisions or improve customer experience.

3. Bar Raiser Round

A Bar Raiser is a specially trained interviewer whose role is to ensure that the candidate meets Amazon’s high hiring standards. This round usually covers both technical and behavioral questions, and the Bar Raiser will assess your long-term potential at Amazon, not just for the specific role you’re interviewing for.

Summary:

Phone Screen: 1-2 rounds (technical + behavioral)
Onsite/Virtual: 4-5 rounds (SQL, Data Modeling, Problem-solving, Behavioral, and Bar Raiser)

The entire process can take anywhere between 4 to 6 weeks from start to finish.

1.SQL Questions for Amazon BIE role

Amazon business intelligence engineer interview questions

Basic Queries:

Write a query to find all customers who placed an order in the last 30 days.
How would you retrieve distinct values from a column in a table?

JOINs:

Write a query to retrieve the total amount spent by each customer, using customers and orders tables.
How would you get the list of customers who have placed an order, but don’t have a shipping address, using orders and addresses tables?

Aggregate Functions:

Write a query to calculate the total revenue generated in each month from the sales table.
How would you find the average order value per customer from the orders table?

GROUP BY and HAVING:

Write a query to find the top 5 customers by total spend from the orders table.
How would you find the products that have been ordered more than 100 times in the last year?

Subqueries:

Write a query to find the second highest salary from the employees table.
How would you use a subquery to retrieve customers who have placed more than three orders?

Window Functions:

Write a query using a window function to rank products by total sales within each category.
How would you calculate a running total of sales for each product?

Data Manipulation (UPDATE, DELETE):

Write a query to update the status of all orders that are more than 30 days old to ‘Archived’.
How would you delete duplicate rows in a table, keeping only the latest entry?

Complex Filtering (WHERE, BETWEEN, IN, LIKE):

Write a query to find all orders placed by customers whose names start with ‘A’ and who made purchases in the last 6 months.
How would you retrieve orders that have a value between $50 and $100?

Optimization and Indexing:

How would you optimize a slow SQL query? What steps would you take?
Given a table with millions of rows, how would you ensure your query runs efficiently?

Data Transformation (CASE, COALESCE, IFNULL):

Write a query using CASE to categorize orders based on their total value (e.g., ‘Low’, ‘Medium’, ‘High’).
How would you handle null values in a query when calculating the total revenue?

2. What are the steps you take to troubleshoot a slow SQL query?

Answer: To troubleshoot a slow SQL query, I would:

Examine the execution plan: Check where the bottlenecks are (e.g., full table scans, improper joins).
Check indexing: Ensure the relevant columns are indexed and that the query uses the indexes properly.
Use appropriate joins: Ensure that I am using the correct types of joins (inner, left, etc.).
Optimize subqueries: Check if subqueries can be replaced with JOINs or Common Table Expressions (CTEs).
Limit the dataset: Avoid pulling unnecessary columns or rows, use filters like LIMIT and WHERE clauses.
Check hardware and server performance: Look into server performance, I/O issues, or network problems.

3. How would you design a data model for a retail business?

Answer: For a retail business, I would design the data model to accommodate:

Fact Tables: These would include transactional data, such as sales or returns. Each row would represent a single transaction and include fields like Transaction ID, Product ID, Customer ID, Store ID, Transaction Date, and Amount.
Dimension Tables: These would provide contextual information for the fact tables, such as:
- Products Dimension: Product details like Product ID, Product Name, Category, Price, and Supplier.
- Customer Dimension: Customer information like Customer ID, Name, Location, Gender, Age, and Email.
- Store Dimension: Store details like Store ID, Store Name, Location, and Manager Name.
- Time Dimension: Allows for detailed time analysis (e.g., Year, Month, Quarter, Day).

This schema supports fast querying and analytics, such as total sales by store, average transaction amount by customer, etc.

4. How do you ensure data quality and integrity in BI reporting?

Answer: To ensure data quality and integrity:

Validate data sources: Ensure that the data from various sources is accurate and complete.
Use ETL tools effectively: Implement error checks and validation steps in the Extract, Transform, Load (ETL) process.
Define data quality rules: Set business rules to check for data inconsistencies, such as duplicate entries, missing values, and outliers.
Automate data quality checks: Use automated processes to regularly check the consistency and accuracy of the data.
Ensure proper documentation: Document data sources, transformations, and assumptions for transparency and to maintain data lineage.
User feedback loops: Continuously gather feedback from business users to ensure the data meets their needs and is understood correctly.

5. How would you handle a request for a new BI report that requires data from a system you are unfamiliar with?

Answer: In such a scenario:

Understand the report requirements: I would first clarify the goals of the report and the metrics needed.
Explore the unfamiliar system: I would research the system, reviewing its documentation and schema to understand how the data is stored and how to access it.
Collaborate with system experts: I’d reach out to engineers or analysts familiar with the system for guidance on extracting the relevant data.
Test data extraction: I would pull a small sample of data from the system to ensure I understand the structure before proceeding with the full report.
Incorporate the data into the report: Once confident, I’d integrate the data into the existing BI system or create a new report using the data.

6. What are common performance issues in dashboards, and how do you optimize them?

Answer: Common performance issues in dashboards include:

Large data loads: Pulling too much data can slow down performance.
Complex calculations: Excessive calculations or aggregations done in real time can cause delays.
Improper visualization types: Using complex visualizations, such as maps or scatter plots, with large datasets.

To optimize dashboard performance:

Use aggregated data: Pre-aggregate data in the ETL process, so the dashboard doesn’t have to perform real-time calculations.
Limit the data displayed: Apply filters or reduce the date range to only show the most relevant data.
Optimize queries: Ensure the underlying SQL queries are efficient, using indexes, limiting joins, and reducing subqueries.
Cache results: Cache frequently used data to reduce the time needed for repetitive queries.
Optimize visualizations: Choose simpler chart types that load faster and are easier to interpret.

7. How do you approach working with stakeholders who may not have a technical background?

Answer: When working with non-technical stakeholders:

Simplify explanations: Use simple language to explain technical concepts without jargon.
Use visual aids: Use diagrams or analogies to make complex ideas more understandable.
Understand their needs: Focus on understanding their business goals and how data can support decision-making, rather than diving into technical details.
Iterate with feedback: Continuously get feedback to ensure that what I’m delivering aligns with their expectations.
Provide training: Offer basic training on BI tools to help them feel more comfortable interpreting and using the data.

8. What’s your experience with AWS services like Redshift, Athena, and QuickSight?

Answer: In AWS, I have used:

Amazon Redshift: For building and maintaining data warehouses. I have optimized queries and used its massive parallel processing (MPP) capabilities to handle large datasets efficiently.
Amazon Athena: For querying data stored in Amazon S3 using SQL without needing to load it into a database. It’s great for quick, ad-hoc analysis.
Amazon QuickSight: For building interactive dashboards and reports. I like that it integrates seamlessly with other AWS services and allows for easy sharing of insights with stakeholders.

Amazon 14 Leadership principles

Amazon business intelligence engineer interview questions
Amazon’s 14 Leadership Principles serve as the foundation for its culture and decision-making. Below are the principles along with examples to illustrate how they apply in a practical context:

1. Customer Obsession

Amazon leaders start with the customer and work backwards. They strive to earn and keep customer trust.

Example: When Amazon launched the Kindle, the company obsessively focused on customer feedback. They gathered insights on how readers consumed books and incorporated these features into the Kindle device, focusing on ease of use, portability, and access to a wide range of books.

2. Ownership

Leaders act on behalf of the entire company, beyond just their own team. They never say, “that’s not my job.”

Example: An Amazon manager noticed an issue with delayed shipments in a department that wasn’t under their control. Instead of ignoring it, they took the initiative to collaborate with the team involved to solve the problem, even though it wasn’t directly their responsibility.

3. Invent and Simplify

Leaders look for ways to innovate and simplify processes.

Example: Amazon’s one-click purchase button is a great example of simplifying the customer buying process. By reducing the number of steps in the checkout process, Amazon made it easier for customers to complete a purchase, which also increased conversion rates.

4. Are Right, A Lot

Leaders are expected to have strong judgment and good instincts. They seek diverse perspectives and work to disconfirm their beliefs.

Example: Amazon Prime’s launch in 2005 was initially met with skepticism. Some leaders believed offering free two-day shipping for a flat annual fee would be too costly. However, Jeff Bezos and his team trusted their instincts, and it became a major success, driving long-term customer loyalty and increasing sales.

5. Learn and Be Curious

Leaders are never done learning and always seek to improve themselves.

Example: Amazon Web Services (AWS) was born out of Amazon’s curiosity to leverage its technical infrastructure. They asked, “How can we use our cloud computing power to help other businesses?” This question led to one of Amazon’s most profitable business units.

6. Hire and Develop the Best

Leaders hire the best talent and mentor them to grow.

Example: Amazon has a rigorous interview process, often referred to as the “bar raiser” program, where interviewers are trained to assess candidates not just for current role fit but for future leadership potential. This ensures the company continuously brings in talent that can grow within the organization.

7. Insist on the Highest Standards

Leaders continually raise the bar and drive their teams to deliver high-quality products and services.

Example: Amazon’s commitment to delivering packages on time is part of its insistence on the highest standards. To meet customer expectations, the company constantly improves its logistics, fulfillment centers, and last-mile delivery operations.

8. Think Big

Leaders create and communicate a bold direction that inspires results.

Example: The concept of Amazon Prime started as a simple subscription service for free two-day shipping. However, Amazon thought bigger by expanding it into a multi-service offering, including streaming content (Prime Video) and other perks, making it a key part of their customer loyalty strategy.

9. Bias for Action

Leaders take calculated risks and value speed. Many decisions and actions are reversible and do not need extensive study.

Example: Amazon is known for making quick decisions on experiments. For instance, Amazon launched Amazon Go, a cashier-less store, as a quick prototype rather than waiting for perfect technology. They took action and refined the technology through real-world testing.

10. Frugality

Leaders accomplish more with less. Constraints breed resourcefulness and innovation.

Example: Amazon is famous for its frugality, especially in its early years. For instance, in its early days, Amazon used door desks (desks made from cheap wooden doors) to keep costs low, emphasizing the principle of being resourceful and focusing on value rather than luxuries.

11. Earn Trust

Leaders listen attentively, speak candidly, and treat others with respect.

Example: Amazon’s commitment to transparency is evident in its customer service policies. For example, when customers report issues with their orders (such as damaged goods), Amazon often provides immediate refunds or replacements, building trust through quick, effective solutions.

12. Dive Deep

Leaders operate at all levels, stay connected to the details, and audit frequently.

Example: Jeff Bezos is known to have dived deep into customer feedback data. He would personally read customer complaint emails and pass them to teams for detailed investigation. This attention to detail helps ensure that leaders at Amazon remain closely connected to customer needs.

13. Have Backbone; Disagree and Commit

Leaders respectfully challenge decisions when they disagree, even when doing so is uncomfortable. Once a decision is made, they commit wholly to it.

Example: When launching the Fire Phone, some Amazon leaders had reservations about entering the competitive smartphone market. Despite voicing their concerns, once the decision was made, they committed fully to the project. Although the phone ultimately failed, the team was united in delivering it.

14. Deliver Results

Leaders focus on the key inputs and deliver them with the right quality and in a timely manner.

Example: Amazon’s ability to launch new features and services quickly—such as Amazon Prime Now, which offers one-hour delivery—demonstrates its commitment to delivering results that matter to customers, focusing on speed and reliability.

These 14 principles guide Amazon employees in decision-making and maintaining a customer-centric focus while driving innovation and growth across all areas of the business.

Amazon business intelligence engineer interview questions

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