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As one of the leading technology companies across the globe, Amazon leverages big data to improve its extensive E-Commerce platform. Data on every customer interaction is gathered and analyzed– from the discovery of a product, to its purchase, and even to its eventual delivery. To refine site performance and improve customer experiences, Amazon feeds this data into its recommendation system, which displays the most relevant products for each customer.
The process of creating a personalized screen of recommendations extends to all of Amazon’s products and services, including Alexa, Prime Video, and Twitch. Business intelligence engineers translate the large data warehouse at Amazon into meaningful insights and improvements. At Amazon, data drives every decision.
The Business Intelligence Engineer Role at Amazon
At Amazon, the Business Intelligence Engineers work in tandem with clients, analysts, and database developers to translate collected data into business decisions. The solutions created by these engineers assist in the analysis, automation, and reporting of both internal and external client data.
See if you can find the relationship between BI Engineers and Business Analysts at Amazon by reading "The Amazon Business Analyst Interview" on Interview Query!
Frequently, Amazon engineers are embedded within teams and work cross-functionally with other teams, aiming to improve overall customer experience. The roles of this position range from implementing solutions through data modeling to providing guidance to business leaders.
BI Engineering Teams at Amazon
Amazon is large enough to boast of over 40 departments with more than 100 internal teams within these departments. Therefore, it is crucial for Amazon to efficiently process and analyze the huge amounts of corporate data it receives. Business Intelligence Engineers design software and corporate platforms to do exactly this, thereby making it easier to draw meaningful conclusions from Amazon’s collected data. They work within teams and alongside Amazon’s internal clients to provide accurate and accessible data that supports critical business processes.
Based on the teams assigned to, Business Intelligence Engineers may perform functions such as:
- Transportation Risk & Compliance (TRC): Meticulously analyze data to find ways to keep Amazon's customers and partners safe according to safety compliance protocols. Use predictive ML models to search for safety-related trends in the data.
- Payment: Leverage huge amounts of customers’ payment data to improve the payment experience for customers. Use probabilistic models and Amazon's technological infrastructure to display the offerings to customers in real-time.
- Amazon Care: Develop data-driven models to shape the future of Amazon Care. Collaborate with business and finance teams to improve Amazon decision-making processes.
- Automated Inventory Management: Develop analytics and prediction tools to accurately quantify the shopping experience of every Amazon customer. Work closely with Amazon's internal customers (such as the software development team) to test and validate these tools at scale.
- Logistics Business Intelligence and Analytics: Work with one of the largest and most complex data warehouses in the world. Mine data, gather customer insights, make recommendations, and help leaders make sound business decisions based on trends in data. Partner with software engineer teams to streamline or automate this business intelligence process.
- Prime Video (Catalog Systems and Operations organization): Define useful business metrics, develop self-service reporting program, conduct experiments that can inform decisions about the structure of Amazon Prime’s Video Catalog.
- Alexa: Use Alexa’s data source to define and automate key business metrics and leverage advanced data analytics to understand customer behavior. Design experiments (A/B experimentation) to test customer-engagement strategies.
- AmazonFresh / Prime Now Customer Insights & Analytics: Use data to help Amazon gain a better understanding of customers behavior. Develop new and innovative analysis techniques to inform product strategy, design in order to maximize customer engagement.
The Amazon BI Engineer Interview Process
The Amazon business intelligence interview is similar to most other technical interviews at Amazon, consisting of two initial phone interviews followed by an onsite interview.
The initial screen is mostly a discovery interview to find out if you are a good fit for the culture and the company. The interviewer will ask you questions about your background and experience. You must be ready to discuss your past relevant projects with the interviewer. Tell them about any challenges, unique situations or problems you faced and how you overcame them.
- Give me an example of when you used data to make a decision that solved a problem.
- Tell me about the most complex problem you’ve ever worked on.
- Tell me about a time where you not only met a goal but considerably exceeded expectations.
- Tell me about a time when you were able to deliver an important project under a tight deadline.
- What are the variables you would consider when you are going to predict demand for a product?
The technical will be conducted via a live coding platform. The interview tests for SQL and Python skills. Expect around five SQL problems that will steadily increase in difficulty. It's definitely helpful to practice the Amazon SQL questions on Interview Query.
This is the last interview stage and comprises of 5 one-on-one interview rounds with business intelligence engineers, data scientists, and a hiring manager. Each interview round lasts approximately 45 minutes with a lunch break in-between.
Questions in this interview span across the breadth of data science concepts and are scenario-based (with real Amazon problems). Each interview round has one form of behavioral question and you can expect a lot of “Tell me about a time…” questions.
Overall, the onsite interview is broken down into:
- Statistics and SQL technical interview with a Business Intelligence Engineer. This interview assesses your SQL and basic statistics knowledge. It also has some behavioral elements to it.
- Behavioral plus SQL interview with some case based questions around leadership principles.
- Case Study Interview and behavioral interview with a hiring manager.
For an in-depth example of a case study question, go through the "Amazon Business Intelligence Case Question: Duplicate Products" article on Interview Query!
- Behavioral Interview with a focus on leadership principles
- Statistics and Product-Sense interview with a data scientist. This interview round assesses your basic knowledge of data science concepts and metric definition.
BI Engineering Interview Notes and Tips
The Amazon BI interview process is designed to learn candidates ability to leverage data warehouse information and advanced analytics tools to turn data into meaningful information that can be used to make sound business decisions. This interview is a combination of various data science theories and it tests the depth of your domain knowledge as it applies to real-life situations.
Brush up on your knowledge of statistics and probability, SQL and ETL processes, and Business Intelligence solution platforms, such as Tableau, MicroDesktop, SQL SSIS and AWS.
Also, note that Amazon asks situation-based questions and it will help to form your responses around the “STAR” format. It will also help to be familiar with Amazon’s 14 leadership principles and have a story based around these leadership principles.
STAR stands for:
- Situation. What was the situation you or your previous employer faced?
- Task. What tasks were involved?
- Action. What actions did you take?
- Results. What were the results of those actions?
Here's an example, recently featured in The Guardian, of what it sounds like when applying the STAR Method in the interview:
Situation: “A customer rang up complaining that they’d waited more than two weeks for a reply from our sales team regarding a product query.”
Task: “I needed to address the client’s immediate query and find out what went wrong in the normal process.”
Activity: “I apologized, got the details and passed them to our head salesperson, who contacted the client within the hour. I investigated why the query hadn’t been answered. I discovered that it was a combination of a wrong mobile number and a generic email address that wasn’t being checked. I let the client know and we offered a goodwill discount on her next order."
Result: “The client not only continued to order from us but posted a positive customer service tweet.”
Interested in learning more about the Amazon interview process? Check out "The Amazon Data Analyst Interview" on Interview Query!
Amazon Business Intelligence Engineer Interview Questions
- The probability of a product coming from location A is 0.8 and from location B is 0.6. What is the probability that customers will receive the product from location A or location B?
- If there is a project that you have never worked on before, how would you tell that the data you are using for this project is correct and you'll get the correct results?
- What is the difference between OLTP and OLAP?
- How would you set up an online A/B testing scenario, and how would you determine whether or not to roll out a new program given the results of the experiment?
- What are the different ways of query optimization and performance tuning?
- Describe a join to a non-technical person
- You have a website and you need to report the traffic insights on this website to the Product Manager. Write an SQL query to find the top 10 persons who have visited the website in the last month.
- How is variance calculated in PCA?
- What are the assumptions in a random forest model?
- If we want to know whether people like a product, is there any statistical problems you can see when the analysis is based on their behavior on the website?