If you need professionals to make data accessible for analysis, consider hiring data engineers. These professionals can build systems that gather, manage, and convert raw data into usable information.
However, not every data engineer is suitable for you. Here are 10 Amazon data engineer interview questions you can ask to determine whether a potential hire is best for your company. If you’re still struggling with how to find and hire data engineers, start with Revelo, and we can provide vetted candidates and walk you through the interview process to make it easy.
What Is a Data Engineer
Before we dive into 10 sample data engineer interview questions, let's explore what data engineers do.
Data engineers create systems that gather, manage, and convert raw data into actionable information for business analysts and data scientists. They aim to make data accessible so organizations can use it to analyze and optimize performance and sales.
What does a data engineer do? The daily responsibilities of a data engineer include:
- Acquiring datasets that fit business needs
- Creating, testing, and maintaining databases and database pipeline architectures
- Creating algorithms to transform data into useful information
- Building and testing new data analysis tools and validation methods based on client and stakeholder requirements
- Creating and deploying statistical models
- Improving performance, system reliability, and quality
- Maintaining data quality by cleaning, monitoring, and validating data streams
- Complying with relevant security and data governance guidelines
General Data Engineer Interview Questions
Before you ask in-depth technical questions, you should ask general interview questions about candidates' work experience and personality. Examples of general Facebook data engineer interview questions include:
1. What Data Engineering Skills and Education Do You Have?
This question reveals the candidate's data engineering education and experience.
Most data engineers should have the following qualifications and skills:
- At least a bachelor's degree in data science, computer science, mathematics, or a related field
- Professional experience in data engineering or software engineering
- Programming languages like Java, Scala, Python, R, NoSQL, and SQL
- Relational and nonrelational databases
- Data storage solutions, such as data lakes and data warehouses
- ETL (extract, transform, and load) systems
- Big data tools like MongoDB, Hadoop, and Kafka
2. What Makes You the Best Candidate for This Position?
This question reveals why the candidate wants to work for your company. It will also demonstrate the candidate's:
- Passion for data engineering
- Confidence in their data engineering skills and work ethic
- Unique attributes and experiences that make them stand out from the crowd
3. How Many Years of Experience in Data Engineering Do You Have?
If you're hiring for a junior or entry-level position, you should hire someone with zero to three years of experience. However, if you're hiring a senior data engineer, the candidate should have over three years of relevant experience.
4. What Are the Daily Responsibilities of a Data Engineer?
This question reveals whether the candidate understands your job requirements. The ideal answer varies depending on your job description. However, you should generally expect the candidate to cover all or most of the daily responsibilities in the "Data Engineer Definition."
5. What Drew You to Data Engineering?
This question reveals why the candidate chose data engineering as a career. The ideal candidate should be self-driven, passionate, and devoted to data engineering. Here's what a good answer could look like:
I chose to become a data engineer because I love working with data. I also love making others' lives easier — that's why I am passionate about creating algorithms to transform data into useful information. I also happen to be an incredibly disciplined person, which makes me perfect for this role. In my previous roles, I have always met deadlines on and ahead of time so my teammates have more time to retrieve, analyze, and derive useful insights from data. Additionally, I have taught data scientists, C-suite executives, and others to use my data analysis tools.
Data Engineer Technical Interview Questions
After you've asked the general data engineer interview questions above, ask these technical interview questions to get a better understanding of the candidates' technical skills.
6. Walk Me Through A Project You Finished
This should prompt the candidate to explain the following about a project they finished:
- How the project started
- What business problem(s) they were solving
- How they accessed the raw data and converted it into actionable insights
- What programs, coding languages, and other tools they used
- What algorithms they developed to achieve their goals
Once the candidate has walked you through their project, ask the following questions to learn about their work ethic and personality:
- How long did it take you to create this project? Who did you work with?
- What was the creation process like? What barriers did you have to overcome?
- Why did you enjoy working on this project?
- Who did this project help? Did it help business analysts and data scientists? How so?
- What kind of insights did this project produce?
- Did you teach anyone how to use this project?
- What did your manager or the C-suite say about this project?
7. What ETL Tools Have You Worked With? Which Is Your Favorite?
Data engineers use ETL tools to reduce errors, automate, and accelerate data integration.
Ask these Meta data engineer interview questions to learn which ETL tools the candidate has mastered and which are their favorites. Here's a list of common ETL tools they may mention:
- Informatica PowerCenter: This artificial intelligence (AI)-powered ETL tool supports cloud-based and on-premise ETL requirements. It includes modules for data lake, data warehouse, and analytics solutions.
- Microsoft SQL Server Integration Services (SSIS): SSIS lets data engineers develop high-performance data integration, migration, and transformation solutions at low costs. It includes ETL functions for data warehousing. Data engineers can also use it for mining data, loading data into warehouses, copying and downloading files, and administering SQL Server data or objects.
- Integrate.io (formerly called XPlenty): This is a low-code data integration platform for gathering, processing, and analyzing eCommerce data. Data engineers can use it to extract data from any RestAPI-enabled source. They can also use it to build a RestAPI using the Integrate.io API Generator.
8. What Is the Difference Between a Data Lake and a Data Warehouse?
The best data engineer for your company should know the difference between data lakes and data warehouses. Here's a sample answer:
Data lakes contain all of a company's data in an unstructured, raw form, and can store the data for future or immediate use. Meanwhile, data warehouses contain structured data that has been processed.
The data in data warehouses is ready for strategic business analysis. Data from data lakes is usually used by data engineers and scientists who prefer studying raw data to gain unique insights. In contrast, data in data warehouses are typically accessed by business-end users and managers seeking to gain insights from business key performance indicators (KPIs), since the data has already been cleaned and structured to provide answers.
Processing is also different for the data in data lakes versus data warehouses. For data lakes, I would use ELT (Extract, Load, Transform) to extract data from sources for storage in the data lake. However, for data warehouses, I would use ETL (Extract, Transform, Load) to extract data, clean it, and structured it for business-end analysis.
9. What Is Hadoop?
Apache Hadoop is an open-source tool for storing and processing large datasets. Data engineers should know how to use Hadoop to manage and store big data.
An ideal answer should cover the following:
Hadoop, also known as Apache Hadoop, is an open-source framework that allows multiple computers to analyze large datasets in parallel. Data engineers and other professionals use it to efficiently store and process big datasets. Hadoop consists of four modules:
- Yet Another Resource Negotiator (YARN): YARN organizes and monitors resource usage and cluster nodes. It schedules tasks and jobs.
- Hadoop Distributed File System (HDFS): HDFS is a distributed file system that runs on low-end or standard hardware. It offers superior data throughput than traditional file systems as well as native support of large datasets and high fault tolerance.
- Hadoop Common: This offers common Java libraries that can be used across modules.
- MapReduce: This framework helps software perform parallel computation on data. Specifically, it morphs input data into datasets that can be computed in key value pairs.
The Hadoop ecosystem has expanded significantly in recent years. Today, it encompasses many applications and tools for gathering, storing, analyzing, processing, and managing big data, including Spark, Hive, Presto, HBase, Hive, and Zeppelin.
10. What is SQL? How Is It Related to Data Engineering?
This question reveals the candidate's knowledge of SQL or Structured Query Language. Data engineers can use SQL to access and manage databases.
A good answer could look like this:
SQL or Structured Query Language is a domain-specific language for managing data in relational database management systems. A relational database stores data in tabular form, with columns and rows representing various data attributes and the different relationships between data values. As a data engineer, I can use SQL statements to update, store, search, delete, and retrieve data from the relational database. I can also use SQL to optimize and maintain database performance.
Besides this question, you should also ask other SQL interview questions for data engineers. You should also ask other language and platform-specific questions, such as Python interview questions for data engineers and Azure data engineer interview questions.
How to Hire Data Engineers
To hire suitable data engineers for your team, you must ask the right data engineer interview questions. Not only do these questions reveal data engineers' experiences and skills, but they also reveal their motivations, passions, and work ethic. You can then use these answers to ensure you're investing in the most competitive talent for your team.
Before you create and ask interview questions, however, you need to source data engineers, which is often the most difficult part of the hiring cycle. Fortunately, Revelo is here to help. As Latin America's premier tech talent marketplace, we can help you hire the right talent for your team in a cost-effective and timely manner. We have a rigorously pre-vetted talent pool of remote Latin American data engineers with the experience and skills to store, clean, and derive insights from data.
Interested in learning more about how Revelo can help you? Fill in this form to start hiring, and we will match you with the best matches in three days.