data-engineer-amazon

Amazon/AWS Data Engineer Interview Questions

John H.

John H.

I love iced coffee, cute pictures of dogs, and SQL. I've previously worked at Big Tech as a data analyst and now spend my time writing and helping job seekers ace their big tech interview @ bigtechinterviews.com.

Amazon Data Engineer Interview Questions

 

In this guide, I’ll provide comprehensive insights into the Amazon Data Engineer interview process, including common questions you might encounter, tips on interview preparation, and the qualities Amazon seeks in an ideal candidate. As one of the world’s largest tech companies, Amazon is continuously seeking top talent. If you’re aspiring to secure a position as a data engineer, mastering this interview is crucial. This guide is your go-to resource for Amazon Data Engineer Interview Questions and essential information to excel in the process.

Table of Contents:

  • Overview of the Data Engineer Interview Process
  • Amazon Data Engineer Interview Question Examples
  • Tips for Amazon Data Engineer Interview Preparation
  • Frequently Asked Questions

Overview of the interview process

The Amazon data engineer interview process and timeline are similar to other Big Tech or FAANG companies with around one to three months from initial application to offer. What makes Amazon’s data engineer interview unique is the focus on Amazon’s 16 leadership principles and the Amazon customer experience. Amazon puts a lot of emphasis on these two areas to ensure that their data engineers will be able to work Amazon’s unique company culture and values into their projects.

FYI: Amazon added two new leadership principles in 2019 - customer obsession and ownership.

The Amazon data engineer interview process typically consists of three rounds:

  • Phone screen with a recruiter
  • Technical phone screen with engineer
  • Onsite interview with multiple stakeholders

The phone screen with the recruiter with primarily revolved around understanding your background, experience, and expectations to ensure you’re qualified and aligned to proceed with the role. The recruiter might ask a few questions such as “Why did you apply for Amazon?” or “What are you looking for in a data engineering role?” to get a better sense of your goals and motivations.

The technical phone screen which lasts about an hour is conducted by an Amazon engineer and will assess your technical abilities. The format includes: SQL, Data Warehousing and Coding (You can chose any language).

For example, a SQL question asked during the Amazon Data Engineer interview might be

“Given two tables Product and Orders, Can you find out which Product was sold the most in the year 2020?”.

Answer: SELECT ProductID, SUM(OrderQty) as TotalSold

FROM Orders

JOIN Product ON Orders.ProductID = Product.Product

For data warehousing, a question might be, “What are the pros & cons of using AWS Redshift over other DataWarehousing solutions (Oracle, MySQL, etc)”

Answer: Some pros of using Redshift are that it is easy to set up and use, it is less expensive than other data warehousing solutions, and it integrates well with other Amazon services. Some cons of using Redshift are that it is not as fast as some other data warehouses, and it does not support some SQL features (e.g. user-defined functions).

As for coding questions, Amazon will give you a problem to solve in the language of your choice. The interviewer will then ask you to walk them through your code and explain your thought process. Amazon engineers are looking for candidates For example, “Given a list of numbers [1,2,3,4,5,6,7,8,9], print all the pairs for which the summation of index positions is equal to 10”

Answer: (1,9),(2,8),(3,7),(4,6),(5,5)

Your final round will be an onsite with Amazon managers, engineers, and other stakeholders. These Amazon data engineer interview questions will assess not only your technical skills but also Amazon leadership principles. Amazon managers want to see if you’re able to work independently, take ownership of projects, and deliver results. Amazon engineers will assess your problem-solving skills and coding abilities. Amazon stakeholders like the bar raiser will want to see if you have the potential to be an Amazon leader and role model.

Amazon Data Engineer Interview Question Examples

Let’s get into the key categories of questions you’ll encounter during the Amazon data engineer interview.

  • Behavioral interview questions
  • SQL interview questions
  • Database management interview questions
  • Coding interview questions

Amazon Data Engineering Behavioural Interview Questions

Amazon behavioral interview questions assess your ability to work Amazon’s unique company culture and values into your projects. At the core of the interview, they’ll be assessing your cultural fit through the lens of the 16 leadership principles.

The leadership principles are a guiding set of Amazon values that all Amazon employees are expected to uphold. For example, Amazon’s customer obsession principle states that “leaders start with the customer and work backwards.” Amazon managers will want to see if you’re able to put the customer first in your work and how you go about solving customer problems.

Behavioral Interview Questions

  • Can you tell me about your experience in data engineering?

Example Response: I have experience in data engineering from my previous job as a data engineer. I was responsible for designing and building the data pipeline that fed our analytics team. I also created ETL jobs to clean and transform data from our database into the format that our analytics team could use.

  • How have you demonstrated customer obsession in your work?

Example Response: In my previous job, I was responsible for designing and building the data pipeline that fed our analytics team. I worked closely with the analytics team to understand their needs and created ETL jobs to clean and transform data from our database into the format that they could use. I also created a dashboard to help them track the progress of the data pipeline and identify any issues.

  • How have you approached a problem from a customer perspective?

Example Response: In my current company, I work on a team that is responsible for designing and building the data pipeline that feeds our analytics team. I was recently assigned to work on a new feature that would allow our customers to track their order history. I started by talking to some of our customers to understand their needs. I then designed and built the data pipeline and ETL jobs necessary to implement. By doing this, I was able to build a solution that met the needs of our customers.

  • What Amazon values do you think are most important for data engineers?

Example Response: Amazon values customer obsession and innovation. As a data engineer, I think it’s important to always keep the customer in mind and design solutions that meet their needs. It’s also important to be innovative and always look for ways to improve the data pipeline.

  • How have you integrated Amazon values into your work?

Example Response: Amazon values customer obsession and innovation. As a data engineer, I always keep the customer in mind and design solutions that meet their needs. For example, in my previous job, I was responsible for designing and building the data pipeline that fed our analytics team. I worked closely with the analytics team to understand their needs and created ETL jobs to clean and transform data from our database into the format that they could use. I also created a dashboard to help them track the progress of the data pipeline and identify any issues.

Amazon Data Engineering SQL Interview Questions

The Amazon Data Engineer technical interview typically consists of two to three SQL questions covering SQL concepts such as joins, window functions, and subqueries.

Joins

What is a join?

A join is a way to combine data from two or more tables into a single table.

What are the different types of joins?

The different types of joins are inner joins, outer joins, left outer joins, right outer joins, and full outer joins.

What is the difference between an inner join and an outer join?

An inner join returns only rows that have a match in both tables. An outer join returns all rows from one table and matching rows from the other table.

What is the difference between a left outer join and a right outer join?

A left outer join returns all rows from the left table and matching rows from the right table. A right outer join returns all rows from the right table and matching rows from the left table.

What is a full outer join?

A full outer join returns all rows from both tables, even if there are no matches in the other table.

Window Functions

What is a window function?

A window function is a type of SQL function that can be used to perform calculations on a set of rows.

What are the different types of window functions?

The different types of window functions are aggregate functions, ranking functions, and analytic functions.

What is an aggregate function?

An aggregate function is a type of window function that can be used to perform calculations on a set of rows.

What is a ranking function?

A ranking function is a type of window function that can be used to rank the rows in a result set.

What is an analytic function?

An analytic function is a type of window function that can be used to perform calculations on a set of rows.

Subqueries

What is a subquery?

A subquery is a SQL query that is used to select data from another table.

What are the different types of subqueries?

The different types of subqueries are correlated subqueries and non-correlated subqueries.

What is a correlated subquery?

A correlated subquery is a SQL query that is used to select data from another table based on values in the first table.

What is a non-correlated subquery?

A non-correlated subquery is a SQL query that is used to select data from another table without using values from the first table.

Let’s put these SQL concepts into practice with Amazon SQL interview questions.

Question 1: Given two tables, orders and order_items, write a SQL query to find the total number of items ordered for each order.


Answer:

SELECT o.id, SUM(oi.item_id)

FROM orders o

JOIN order_items oi ON o.id = oi.order_id

GROUP BY o.id

The logic behind this answer is to first join the two tables together on the order id column. This will give us a table that contains all of the information we need to answer the question. Next, we use the GROUP BY clause to group the rows by order id. Finally, we use the SUM function to calculate the total number of items ordered for each order.

Question 2: Given a table of employees and their managers, write a SQL query to find the name of each employee and their manager.


Answer:

SELECT e.id, e.name, m.name AS manager_name

FROM employees e

JOIN managers m ON e.manager_id = m.id;

This answer uses a similar technique to the first question. We start by joining the two tables together on the manager id column. This will give us a table that contains all of the information we need to answer the question. Next, we use the SELECT clause to choose the columns that we want to include in our output. Finally, we use the AS keyword to rename the manager_name column.

Question 3: Write a query to find all the employees who started before their manager. Please order by employee start_date in ASC order.


Answer:

SELECT e.id, e.name, e.start_date

FROM employees e

JOIN managers m ON e.manager_id = m.id

WHERE e.start_date < m.start_date

ORDER BY e.start_date ASC;

This answer uses a similar technique to the first question. We start by joining the two tables together on the manager id column. This will give us a table that contains all of the information we need to answer the question. Next, we use the WHERE clause to filter for rows where the employee start date is before the manager’s start date. Finally, we use the ORDER BY clause to order the results by employee start date in ascending order.

Amazon Database Management Interview Questions

The types of database management interview questions consist of Amazon Relational Database Service (RDS), Amazon DynamoDB, Amazon ElastiCache, Amazon Redshift, and Amazon Aurora.

Question 1: Amazon Relational Database Service (RDS) is a managed relational database service provided by Amazon Web Services (AWS). What are some of the benefits of using Amazon RDS?

Answer: Amazon RDS provides many benefits over traditional relational database management systems, including the following:

  • Amazon RDS is a managed service, so you don’t have to worry about patching, backing up, or monitoring your database. Amazon RDS takes care of these tasks for you.
  • Amazon RDS provides high availability and failover for your database, so you don’t have to worry about losing data if your primary database instance goes down.
  • Amazon RDS makes it easy to scale your database up or down as your needs change, so you only pay for the resources you use.
  • Amazon RDS provides support for popular databases, such as MySQL, Amazon Aurora, PostgreSQL, and Microsoft SQL Server.

Question 2: What are the pros & cons of using AWS Redshift over other DataWarehousing solutions?

Answer: Amazon Redshift has many benefits over other data warehousing solutions, including the following:

  • Amazon Redshift is a fully managed service, so you don’t have to worry about provisioning, patching, or monitoring your data warehouse. 
  • Amazon Redshift is designed for high performance, so you can easily scale your data warehouse up or down as your needs change.
  • Amazon Redshift integrates with other Amazon Web Services, such as Amazon S3 and Amazon EMR, so you can easily process and analyze your data.

The main downside of Amazon Redshift is its cost. Amazon Redshift is one of the more expensive Amazon Web Services, so you will need to carefully consider whether the benefits justify the cost before using it.

Question 3: What are the different languages present in DBMS?

Answer: The different languages present in DBMS are Data Definition Language (DDL), Data Manipulation Language (DML), and Data Control Language (DCL).

  • Data Definition Language (DDL) is used to define the structure of a database, such as the tables and fields it contains
  • Data Manipulation Language (DML) is used to insert, update, and delete data in a database.
  • Data Control Language (DCL) is used to control access to a database, such as granting or revoking permissions.
  • Transaction Control Language (TCL) is used to control transactions in a database, such as committing or rolling back changes.

Amazon Data Engineer Coding Interview Questions:

The coding interview questions can be solved with the language of your choice. Some pointers to remember during this portion of the interview is to first, think out loud. The interviewer wants to hear your thought process. Second, don’t rush through the code. Take your time and third, if you get stuck, ask clarifying questions.

Question 1: Given a list of numbers [1,2,3,4,5,6,7,8,9], print all the pairs for which the summation of index positions is equal to 10

Answer:

array = [1,2,3,4,5,6,7,8,9]

sum_of_index = 10

for i in range(int(sum_of_index/2)):

if i < len(array) and sum_of_index-i-1 < len(array):

print((array[i], array[sum_of_index-i-1]))

Question 2: Given a list_of_words = [‘hi’, ‘there’, ‘hi’, ‘hello’, ‘hi’] can you find out the frequency of each word?

Answer:

word_count_map = dict()

for word in list_of_words:

word_count_map[word] = word_count_map.get(word, 0) + 1

print(word_count_map)

Tips for Acing your Amazon Data Engineer Interview

1. Do your research

Before your interview, make sure you research Amazon as a company. Familiarize yourself with their mission and values, and try to find examples of how you could personify those in your own work. The Amazon website is a great place to start, but you can also check out Amazon-focused news sites and social media accounts.

2. Be prepared to answer behavioral questions

Amazon likes to ask behavioral questions in their interviews. This means they’ll want to know how you’ve handled certain situations in the past, and what your thought process was. Be prepared to answer these types of questions with specific examples.

3. Practice Amazon-specific SQL questions on Big Tech Interviews

If you’re interviewing for a role such as an analyst, data scientist, BI engineer, or data engineer, make sure you brush up on your SQL skills before your interview using Big Tech Interviews.

4. Dress the part

When you’re interviewing at Amazon, it’s important to dress for the role you’re applying for. Amazon has a casual dress code, but that doesn’t mean you should show up to your interview in jeans and a t-shirt. If you’re unsure of what to wear, err on the side of being more formal.

5. Be yourself

Above all else, be yourself in your Amazon interview. Amazon is looking for candidates who fit their culture, so it’s important that you show them who you are. Be honest, be genuine, and let your personality shine through.

Additional Resources

Frequently Asked Questions

1. What are the 16 Amazon Leadership Principles?

Answer: Amazon has 16 leadership principles that guide their business decisions and actions. These are: customer obsession, ownership, invent and simplify, are right a lot, learn and be curious, hire and develop the best, insatiable curiosity, thinker developer, bias for action, earn trust of others, dive deep, have backbone; stand up for what’s right, leap, deliver results, and be kind.

2. What is the average salary for a data engineer?

Answer: According to Glassdoor, the average salary for a data engineer is $106,840 per year.

3. What is Amazon’s interview process like?

Answer: Amazon’s interview process typically consists of an initial screening interview, followed by one or more phone interviews, and finally on-site interviews. Amazon puts a lot of emphasis on the fit of the candidate with their culture, so behavioral questions are common throughout the process. Candidates should also be prepared to answer Amazon-specific SQL questions.

4. How can I prepare for my Amazon interview?

Answer: In addition to Amazon-specific SQL questions, candidates should also expect to answer behavioral questions. Be prepared to talk about your past experiences and how they’ve shaped you as a leader. Amazon is also interested in candidates who personify their leadership principles, so be sure to familiarize yourself with those before your interview.

5. What should I wear to my Amazon interview?

Answer: Amazon has a casual dress code, but that doesn’t mean you should show up to your interview in jeans and a t-shirt. If you’re unsure of what to wear, err on the side of being more formal.

6. Where can you practice Amazon data engineer questions?

Answer: You can practice free and paid questions on Big Tech Interviews. They have over 25+ Amazon SQL questions.

7. How can I stand out in my Amazon data engineer interview?

Answer: Amazon is looking for candidates who fit their culture, so it’s important that you show them who you are. Be honest, be genuine, and let your personality shine through. Amazon is also interested in candidates who personify their leadership principles, so be sure to familiarize yourself with those before your interview.

8. What does the Amazon Data Engineer actually do?

Answer: Amazon Data Engineers are responsible for Amazon’s data processing infrastructure. This includes developing data pipelines, designing database architecture, and maintaining Amazon’s data warehouse. Amazon Data Engineers also play a role in helping Amazon make data-driven decisions by providing insights from data analysis.

9. Amazon Data Engineer vs Amazon Software Engineer: what’s the difference?

Answer: Amazon Data Engineers are responsible for Amazon’s data processing infrastructure. This includes developing data pipelines, designing database architecture, and maintaining Amazon’s data warehouse. Amazon Data Engineers also play a role in helping Amazon make data-driven decisions by providing insights from data analysis. Amazon Software Engineers, on the other hand, design and build Amazon’s software applications. Amazon Software Engineers also work on Amazon’s web services, which power Amazon’s e-commerce platform.

10. What’s the difference between an Amazon Data Engineer and an Amazon Business Intelligence Engineer?

Answer: Amazon Data Engineers are primarily tasked with designing and maintaining data infrastructure for processing and analyzing large datasets, emphasizing skills in data modeling, ETL processes, and database management. On the other hand, Amazon Business Intelligence Engineers specialize in collecting, analyzing, and presenting business data to support decision-making. Their responsibilities include developing reporting systems, dashboards, and data visualizations, necessitating expertise in data analysis, SQL, and tools like Tableau or Power BI. If you are preparing for an Amazon Business Intelligence Engineer interview, you may encounter questions related to data analysis techniques, SQL proficiency, and experience with data visualization tools. It’s advisable to review common Amazon Business Intelligence Engineer interview questions to ensure thorough preparation for the specific requirements of the role. For the latest and most accurate information, always refer to the official Amazon careers website.

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