Preparing for an upcoming interview can be overwhelming. Whether you are new to the field of big data and seeking to break into a Data Engineering role or an experienced Data Engineer seeking a new opportunity. It’s crucial to be ready for your interview given how fiercely competitive this market is right now. The top Data Engineer interview questions and answers for data engineers are listed below.
We at founderactivity have provided the best list of questions along with explanations of why they are asked. Also the kind of responses that interviewers are typically looking for.
Data Engineer Interview Questions
During an interview, a sizable portion of the questions you’ll be asked will focus on gauging your knowledge of how these crucial systems function. How you’d react to limitations and flaws in their conception and implementation will be also be tested.
Understanding quantitative and analytical techniques to data gathering, processing, and analysis as well as some basic computer science ideas will help you try to get ready for these kinds of queries. If you can describe related projects or applications in your industry, domain expertise is extremely useful.
1. What is Data Engineering?
When working with data, the phrase “data engineering” is used. Data engineering is the term for the primary process of transforming a raw entity of data into useful information that can be used for various purposes. In order to do this, the data engineer must work with the data by gathering data and conducting research on it.
2. What is Data Modelling?
Data modeling is a technique for simplifying complex software design so that it can be easily understood by everyone. It is a conceptual illustration of the relationships between different data objects and the rules.
3. What knowledge do you have of our company and why should we hire you?
By highlighting some exciting facets of the position, the work involved, and the kind of work the company is doing in that area that inspires you to join the company, you can respond to another fundamental question. To demonstrate how all of your experience will make you a better data engineer, emphasize your credentials, experience, skills, and personality.
4. What fundamental abilities are needed to become a data engineer?
Each organization may have its own definition of a data engineer, and they will match your skills and credentials to their evaluation.
If you want to become a successful data engineer, you need to meet the following criteria:
- comprehensive understanding of data modeling.
- having knowledge of database architecture and design. Deep understanding of SQL and NoSQL databases
- working knowledge of distributed systems like Hadoop and data stores (HDFS).
- Skills in data visualization.
- Knowledge of ETL (Extract, Transform, Load) tools and data warehousing.
- You should have strong math and computing abilities.
- Outstanding leadership, problem-solving, critical thinking, and communication skills are advantages.
You can give specific instances of how a data engineer would use these abilities.
5. What function does a data engineer play on a team or in a business?
What they really want to know is: What does a data engineer do?
Recruiters want to know that you understand what a data engineer does in order to answer this question. How do they behave? What function do they fulfill within the team? You should be able to list the typical duties and team members a data engineer collaborates with. If you’ve worked with data engineers in the past as a data scientist or analyst, you might want to mention that.
The interviewer may likewise query:
- The work that data engineers do
- How do data engineers function in a group setting?
- How does a data engineer affect things?
6. Describe the different types of design schemas used in data modeling.
In data modeling, there are primarily two types of schemas: the Star schema and the Snowflake schema. If you are asked to explain one or more of them, elaborate on each one.
In data modeling, there are primarily two types of schemas 1) Star schema and 2) Snowflake schema. If you are asked to explain one or more of them, elaborate on each one.
7. How do structure and unstructured data differ from one another?
Data As data enters the systems in a variety of formats, engineers are constantly working with it. giving them a general classification of structured and unstructured. The methods for storing and accessing these vary. Some of the distinctions are listed below for your convenience.
| Criteria | Structured Data | Unstructured Data |
| Storage | DBMS | Unmanaged file structures |
| Standard | ADO.net, ODBC, and SQL | STMP, XML, CSV, and SMS |
| Integration Tool | ELT (Extract, Transform, Load) | Manual data entry or batch processing that includes codes |
| Scaling | Schema scaling is difficult | Scaling is very easy. |
8. What exactly does a Data Engineer do on a daily basis?
This is a crucial question, so give a thorough response to demonstrate how well you comprehend the role and how much time and effort you have put into learning it. The following ideas should be covered in your answer.
- A data engineer may be involved in any one or more aspects of designing, constructing, and maintaining the data infrastructure, particularly when it comes to systems with enormous storage capacities, such as Big Data.
- Be in charge of the data ingestion and acquisition processes.
- In charge of developing the pipeline for various ETL operations.
- Figuring out how to increase the availability and reliability of data.
9. How would you go about creating a brand-new analytical product?
The purpose of this question is to gauge how well you understand the systems from the ground up. There is no incorrect response to this query, nor is there a perfect response. Answers to the questions listed below may help you find the right one.
- What is the product’s objective?
- What information sources are crucial to the success of the product and the customer?
- What are the available formats and where can I find them?
- How much data is actually being collected?
- What is the prerequisite for the data’s availability, or how accessible do you want your data to be?
- Will the collected data need to be transformed?
- Will you have to react in real-time to the data being ingested?
- Are there currently any data streams involved, or will there be in the future?
After finding the answers to these queries, you attempt to map the technologies that can be used to address the problems and traits of each. This is a strategy you can use to address the interviewer’s initial question; it is not an exhaustive list of questions.
10. Give us an example of an algorithm you recently used.
The algorithm you choose to discuss must be one you are knowledgeable about and, ideally, is one that the business uses. There will be follow-up inquiries to gauge the breadth of your response, such as,
- Why did you decide on this algorithm?
- Does this algorithm scale well?
- What difficulties did you encounter when applying this algorithm? What approach did you take?
11. Do you have any experience converting unstructured data to structured data?
Include in your response the difficulties that arise when going from unstructured to structured.
12. How familiar are you with data modeling?
If you are a qualified candidate with experience, there is a good chance that this question will be posed. Don’t forget to mention the tools you used to create the model and a brief description of your process.
13. What ETL tools have you used and what is your experience with them?
Talk about the tool you chose for ETL and some of its features in your discussion.
Hadoop is a framework that helps to manage the enormous volumes of data that are present in the Big Data ecosystem, and Big Data is a phenomenon brought about by the exponential growth in data availability, storage technology, and processing power. The parts of Hadoop are described below.
- Common MapReduce Hadoop YARN (Yet Another Resource Negotiator)
- YARN (Yet Another Resource Negotiator)
- Hadoop Common
- MapReduce
15. What is a NameNode, and what consequences could a NameNode crash have?
All file metadata is stored on NameNodes for the cluster’s files. Basically, data nodes’ metadata, or details like the location of blocks, file sizes, and hierarchies It is comparable to a File Allocation Table (FAT), which records details regarding the data chunks that make up files and where they are kept on a single computer. The same kind of data is stored by NameNodes in a distributed file system. Even though all of the data blocks are unharmed, a NameNode crash will typically result in data not being available. A high availability configuration will guarantee that there is a passive NameNode that stands in for the active one in the event of failure and takes over.
16. What exactly is a block, and what functions does a block scanner perform?
The Hadoop system generates blocks, the smallest unit of data allotted to a file, automatically for storage in various nodes of a distributed file system. Block Scanner examines the data blocks that are stored on a DataNode to confirm its integrity. The following is a list of some additional Data Engineer interview questions for which you should be ready.
Give the XML configuration files in Hadoop a name.
The following are the XML configuration files that are available in Hadoop:
- Core-site
- Mapred-site
- HDFS-site
- YARN-site
17. What does FSCK mean to you?
The File System Check, or FSCK, the command is a crucial HDFS command. It is typically used when you need to examine files for discrepancies and errors.
18. What do HDFS’s Block and Block Scanner mean?
Hadoop automatically splits large files into smaller units known as blocks when it encounters one. The smallest data entity is thought to be a block. To ensure that the loss-of-blocks created by Hadoop are successfully installed on the DataNode, a block scanner is installed.
19. What does COSHH mean to you?
Classification and Optimization-based Scheduling for Heterogeneous Hadoop Systems is referred to by the abbreviation COSHH. As the name suggests, it enables scheduling to have a positive impact on work completion time at both the cluster and application levels.
20. Briefly explain the Star Schema.
One of the simplest schemas in the Data Warehousing concept is the star schema, also referred to as the star join schema. A star-shaped table structure made up of fact tables and dimension tables are used. When working with massive amounts of data, the star schema is frequently used.
21. What function does Hive serve in the Hadoop ecosystem?
A user interfaces for managing all of Hadoop’s stored data is provided by Hive. HBase tables are used to map the data, which are then modified as necessary. MapReduce jobs are created by running Hive queries, which are similar to SQL queries. By doing this, the complexity of managing multiple jobs at once is kept under control.
22. What exactly do you mean by “rack awareness”?
Rack awareness is the idea that the NameNode makes use of the DataNode to increase incoming network traffic while also reading or writing operations are being performed on the file that is most closely associated with the rack from which the request was made.
23. What are your plans once you start working as a Data Engineer for our company?
Keep your explanation of how you would develop a plan that works with the company setup and how you would implement the plan succinct when responding to these types of Data Engineer interview questions. You would first ensure that it works by first understanding the setup of the company’s data infrastructure. You would also discuss how it could be improved or made better in the coming days with additional iterations.
To gain a little insight into what a day in a life of a data engineer can look like, check out the video below.
Data Engineer Interview FAQs
What can I anticipate from an interview with a data engineer?
A technical phone screen, an HR phone screen, a coding challenge, a take-home exam, an on-site interview, a whiteboard interview for database and system designs, a SQL interview, and finally an “executive” interview to determine cultural fit are all to be anticipated.
There are as few as three stages of interviews at some businesses, while there are as many as nine. Organizations frequently have a high entry barrier to test applicants at every level.
Does demand exist for data engineers?
Yes, data engineers are required by every organization that produces data to build pipelines, manage, and deliver data to various departments. Engineers will be required to extract, transform, and manage the data pipelines and systems as we produce 463 exabytes of data per day by the year 2025.
Write code data engineers?
Yes, even managers who work in the IT industry need to learn how to write code. Python, SQL, Docker, Yaml, and Bash are essential coding languages for data engineers. Code, pipelines, database management, streaming, web scraping, data processing, modeling, and analytics are some of the ways they are used in infrastructure.
What distinguishes a data analyst from a data engineer?
Data engineers gather, modify, and prepare data so that data analysts can derive important business insights. To ensure that high-quality data is available for data analysis tasks like analytical reports, dashboards, customer research, and forecasting, data engineers oversee the entire database system.
What exactly does a data engineer do?
Data can be acquired from various sources, pipelines can be built, validated, and maintained, algorithms can be used to transform data, analytical engineering can be done, compliance with data governance and security is ensured, and entire database systems can be maintained. They are in charge of giving various corporate departments high-quality data streams.
What qualifications are required to be a data engineer?
You need to be skilled in communication, coding, data warehousing, ETL (Extract Transform Load), SQL queries, data analytics, and modeling. Data engineering is a skill that is acquired through practice and overcoming difficult obstacles in the real world.
What salary goals do you have?
According to Indeed, the average pay for data genres in the USA ranges from $116,037 to $299,953. The size, location, and level of experience of the company will all affect your pay. Your base salary, for instance, would be $178,210 per year if you were applying for Meta in Los Angeles and had five or more years of experience. The salary is frequently much lower in Europe, and it is even lower in Asia.
In conclusion, it can be intimidating to prepare for an interview, regardless of whether you’re an experienced Data Engineer looking for a new opportunity or a newcomer to the field of Data Science. You should be ready for your interview given how fierce the current market competition is.
Some of the most typical data interview questions and responses for data engineers are listed above; they will help you prepare for the interviews. One of the best ways to nail your next job interview for a data engineer is to receive formal training and obtain your certification. In order to become a data engineer.
