Top 10 Amazon Data Engineer Interview Questions [with tips and example answers]
Get ready for your Amazon data engineer interview with these example interview questions and job interview tips
If you want to land a job as a data engineer at Amazon, you will face some tough competition. If you want to beat the other applicants, you’ll need to prepare answers for common Amazon data engineer interview questions ahead of time. Practicing your answers before the interview is a great way to make sure you don’t get caught off guard.
In this article, we will explore some common Amazon data engineer interview questions, with explanations and example answers.
Amazon Data Engineer Interview Questions
1. What does a data engineer do?
While this might seem like a basic question to someone interviewing for a data engineering position, employers may ask it to make sure you understand what your responsibilities will be. Employers want to see how well you know your field, and they want to be sure that you can describe the day-to-day work of a data engineer.
A data engineer is responsible for collecting data and analyzing large data sets. They query and manipulate databases to better understand raw data so the organization can use it to make decisions. Data engineers need to understand how to collect data, the best ways to store and display it, and how to extract specific data from a larger dataset.
2. What is data modeling?
No matter what your skill level is, employers will ask you basic questions about data engineering concepts. They want to understand that you can not only understand the basics of data engineering, but explain them in a clear way. When your job is to translate complicated raw datasets into coherent stories, the ability to communicate clearly is a major asset.
A data model is a diagram or visualization of an information system. It maps the collection, flow, and structures of a database. Data modeling is the process of creating these diagrams. Data modeling helps organizations manage their data as a resource and ensure that their information needs are being met.
3. What is the difference between a star schema and a snowflake schema?
In data modeling, there are two main schemas used to develop data warehouses: Star schemas and snowflake schemas. It’s important to understand the two schemas and their different use-cases.
Star schemas are the simplest and most common method of developing data warehouses. They consist of fact tables, containing data for a business process, connected by dimension tables, which structure the data in the way that users can navigate. They are generally used for simple database queries. They are called star schemas because they look like a star shape when represented visually. Star schemes are a subset of snowflake schemas.
Snowflake schemas are an arrangement of tables in which there is a single central fact table connected by multiple dimension tables. The dimension tables are connected to other dimension tables, creating a complex “snowflake” of relationships throughout the database. Snowflake schemas are useful for complex queries and large, complex databases.
4. What is the difference between a relational database and a non-relational database?
There are two types of databases you are likely to encounter as an Amazon data engineer: relational, or SQL, and non-relational, or NoSQL. It’s important to understand the differences between them, and when you would use each one.
A relational database is structured. It organizes data into tables containing columns and rows. The tables in a relational database can be dependent on one another. Relational databases use SQL, Structured Query Language, to navigate the database and retrieve data.
A non-relational database, usually called a NoSQL database, is unstructured. It does not use tables to categorize data. Instead, it stores data in what is essentially a long list, in which items do not have defined relationships to one another. The unstructured information can be more difficult to navigate, but it is more flexible in how it is manipulated.
The best database type to use will depend on the job. When the data you are working with is structured, with clear relationships and dependencies, a SQL database is likely the right tool. If the data is not structured in any clear way, and if different pieces of data have no clear relationships, a NoSQL database is a better choice.
5. What databases do you have experience using?
Beyond establishing that you can communicate data engineering concepts clearly, interviewers will want to know what specific concepts and tools you have experience with. They will likely want to know what database systems you have worked with before, including both SQL databases and NoSQL databases. Make sure to refresh your memory of the different tools you have used as a data engineer.
SQL databases include:
- PostgreSQL
- MySQL
- Oracle
- Microsoft SQL Server
NoSQL databases include:
- MongoDB
- Cassandra
- Amazon DynamoDB
- Apache HBase
6. What are the four Vs of big data?
Big data–the industry built on datasets of a size or complexity too large to be processed using traditional data software–is a field that is important to all large software companies. Most organizations who operate in the modern world strive to make data-driven choices, but the amount of data generated online each day takes immense expertise to understand. As a data engineer, it is your job to understand what big data is and how to harness it to make good business choices.
Big Data is characterized by the “four Vs”. The four Vs of big data are Volume, Velocity, Variety, and Veracity.
- “Volume” refers to the amount of data in a dataset
- “Velocity” refers to the speed at which data is created
- “Variety” refers to the complexity of the data and the diversity of the sources of the data
- “Veracity” refers to the validity and reliability of the data
7. What is Hadoop? What are its features?
Hadoop is a common tool in big data engineering, and it is often used with Amazon Web Services. It is important to understand Hadoop if you want to succeed as an Amazon data engineer.
Hadoop is an open-source data-processing software developed by Apache. It enables networks of computers to work together to process large datasets, rather than relying on a single, powerful computer. This allows the data to be processed more quickly, and ensures that failure is less likely due to the distributed nature of the computation.
Hadoop can be used to collect, store, and process data. It is scalable, fast, and can handle any type of dataset, including structured, unstructured, and semi-structured data.
8. What is a data pipeline?
As a data engineer, you need a strong understanding of how to get data from where it is created into a database where it can be stored and studied. While not all data pipelines are the same, it’s important to understand the basics of how a data pipeline works.
A data pipeline is the series of steps that a dataset goes through from collection to storage and analysis. The specific steps in the data pipeline will depend on the data and what the organization intends to do with it, but the steps in a typical data pipeline might include:
- Data source: data begins as raw data, collected from a database, application, API, or some other source
- Destination: The data is sent to a data store or application
- Governance: Rules, standards, and policies are applied to the data so that it can be maintained and used by the organization
- Transformation: The data may be standardized, verified, sorted, shared, or otherwise manipulated so that it can be effectively analyzed
- Storage: Once transformed, the data is stored where it can be accessed by stakeholders
9. What are your key skills or competencies?
Interviewers will want to know what you, specifically, can bring to their team. There are a lot of data engineers in the world, but they may not be the right fit for Amazon. It’s important to understand what they are looking for, what your best skills are, and what your most marketable data engineering competencies are.
Key data engineering skills include:
- Data modeling
- Database design
- Data visualization
- Cloud data warehousing
- Data lakes
- Extract Transform Load (ETL)
- Machine learning
- Data mining
When answering this question, you should think about what you are best at and what the interviewers may be looking for. These skills don’t mean much on their own, however. It is also important to mention your expertise with specific tools. Common tools used by data engineers include:
- Hevo Data
- Matillion
- Wavefront
- KNIME
- AWS Glue
- Informatica Powercenter
- Flink
- Hadoop
- Kinesis
- Azure
- Tableau
There are, of course, many more. Developing a familiarity with the tools you will be using is integral not only to landing a job as an Amazon data engineer, but succeeding in the role.
10. What is a significant obstacle you have encountered as a data engineer? How did you overcome it?
While technical interview questions may make up the bulk of your amazon data engineer interview, you need to prepare for behavioural interview questions as well. Once your interviewers have established that you have the data engineering chops they’re looking for, they will want to know that you also have the teamwork and leadership abilities to succeed.
When faced with a behavioural question like this one, don’t get too caught up worrying about the specific obstacle you want to discuss. The exact nature of the problem isn’t as important as how you dealt with it. With behavioural questions, interviewers want to know how you solve problems, work with a team, and overcome difficulty. You want to show that you can improvise and adapt to difficult circumstances, or take initiative and lead a team when necessary.
For example, you can tell a story about how you were working on a project where you were going past a deadline or over budget, and how you worked with management to keep stakeholders happy. You could tell a story about how a coworker (or yourself) made a mistake, and how you took accountability and made sure to fix it, and the steps you took to make sure it never happened again. The important thing is that you can show that you can take initiative and deliver results, even when things aren’t going your way.
To prepare for behavioural interview questions, study the STAR method. STAR stands for Situation, Task, Action, Result. It is a strategy for structuring your answer so that it makes maximum impact. It works like this:
- Situation: describe the context for your story, including the company, the team you were on, and your role
- Task: describe the obstacle you had to overcome
- Action: describe the choices you made and the actions you took to overcome the obstacle. Showcase your teamwork skills, leadership skills, or problem-solving skills
- Result: describe how the situation was resolved, and how your hard work or difficult choices paid off
A strong response to behavioural questions can be just as impressive as technical abilities. The story doesn’t necessarily have to have a happy ending, as long as you can show that you handled the obstacle well and learned from the experience. Amazon is always looking for level-headed team members.
Amazon Data Engineer Interview Tips
Preparing for your interview will require more than just writing down answers to common Amazon data engineer interview questions. As your interview nears, consider these tips:
Practice
It’s important to get ready for your interview ahead of time. Even if you are an experienced data engineer, unexpected questions can catch you off guard. Make sure to brush up on important data engineering ideas, including basic concepts and tools. The Amazon data engineer interview process can be long and competitive, so you need to be able to answer any question they throw at you.
Once you’ve found a list of common questions, practice giving your answers out loud. Even if you understand the concepts well, you might not be able to communicate them as clearly as you think. Practice giving your answers in the mirror or in your webcam so you can see what you look like. Appearing confident and personable is an important part of any job interview, especially for competitive roles. If possible, you can even arrange a practice interview with a friend, or an interview coach, to really test your skills.
Research the company
Sure, everyone knows what Amazon is. It’s one of the largest companies in the world. That doesn’t mean you can slack off when you’re getting ready for your interview. Amazon has dozens of different teams and offices around the world, and you should research the department you are interviewing for as much as possible so you know what they are looking for.
Stay positive
If you’ve been at one company or in an industry for a long time, you can start to become jaded and grow out of love with your work. It can be tempting to make fun of some trends in your industry, or complain about the management at your current company.
Never do this in a job interview.
You need to be enthusiastic and forward-thinking at all times. Even if a behavioural interview question requires you to talk about difficult obstacles or uncooperative team members, it’s important to focus on the positives. If you are willing to criticize your current company, interviewers will worry that you will do the same for your next one. Your interviewer should come away thinking that you are a positive and enthusiastic employee, not an annoying whiner.
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