Are you new to the world of data engineering and looking to find an opportunity in a firm in this domain? Or, are you a veteran who is planning a switch? In that case, you need to be prepared for the interview.
Now, preparing for a data engineering interview can be exhausting. However, since this domain of data engineering is competitive, being prepared in advance will give you the upper hand. So, to help you in this endeavor, here is a list of some questions you will likely face in your interview for a data engineer.
Top 25 Data Engineering Interview Questions and Answers
Just have a look at the Data Engineering Questionnaire and see if you are aware of the following concepts.
- Why did you opt for a career in Data Engineering?
This is one of the staple data engineering interview questions. The aim here is to understand your intention and passion behind data engineering. It will also help the interviewer to assess how you will deal with your challenges every day.
How should you answer this question, then? Here is a sample:
I started as a data scientist when I came upon it by chance while studying in college. Then one of my friends also introduced me to online data engineering challenges on websites like Techgig, Leetcode and Kaggle. It got me hooked on data engineering, and I applied for an online challenge on Thoughtworks. Winners would be offered jobs/internships to clear the challenge. I participated and was among the top 3. I was offered an internship for 6 months, where I further polished my data engineering skills.
- What are the core skills required for becoming a Data Engineer?
This is to check whether you understand what the field is all about. Usually, the new candidates, i.e. the fresher, face this question. Here is your template answer –
A data engineer needs to have the following core skills:
- A good grasp of database design and architecture.
- Proficient in both SQL and NoSQL DB systems.
- Experienced in the storage of data stores and understanding distributed systems like Hadoop.
- Experts in Data Warehousing and ETL tools.
- Define Data Modelling
Scientifically documenting complex data is called Data Modelling. It is done with the help of a diagram that gives the system's conceptual representation in the form of a picture. You can also mention any previous experience with data modelling in your career.
- What are the different types of design schemas in Data Modelling?
Mainly, there are two types of schemas in data modelling. Both these data modelling schemas are multidimensional models which are deployed for a data warehouse:
- Star schema: A multidimensional model type which contains fact tables and 2D dimensional tables and uses fewer foreign-key joins. The shape of the model resembles a star.
- Snowflake schema: A multidimensional model type which contains 3D tables, sub-dimension tables and fact tables which resemble the shape of a snowflake.
- What is the difference between a Data Warehouse and an Operational Database?
Databases which use SQL statements like Delete, Insert, and Update have a lot of speed and efficiency. As a result, analysing could be complicated. These are called Operational Databases. In comparison, a data warehouse has calculations, aggregations and select statements which are ideally suited for data analysis.
- What are *args and **kwargs used for?
This question is mainly for experienced data engineers who are applying for advanced roles.
- *args is used to write an ordered function
- **kwargs is used to write unordered arguments used in a function
Moreover, if you can write down the code for these functions by using them in a visual example, then it could show your expertise in this topic.
- What scripting languages do you know? Python, Java or Bash, or anything else?
This question is asked to check your understanding of scripting languages. As a data engineer, you should know scripting languages for efficiently performing analytical tasks and for dataflow automation. But keep in mind, mention that language to the interviewer in which you are most proficient.
- What is the difference between a Data Scientist and a Data Engineer?
The recruiter can judge whether you understand both the job roles, which are integral to the data warehouse team.
Data Engineers develop, test, and maintain the complete architecture for data generation, while data scientists examine and deduce complex data. They primarily focus on an organisation and its conversion of Big Data. Data engineers need to build the infrastructure for data scientists to work.
- How is the validation of data migration done from one database to another?
Data validation means ensuring no data is dropped. There are appropriate validation types in different scenarios. For example, Data validation could either be a simple comparison or it can also be done after the total migration.
- What is NameNode? What happens if NameNode crashes or is terminated?
NameNode is the central node of the Hadoop Distributed File System (HDFS). No actual data is stored in it. Instead, it stores metadata. NameNode is used to track the different files which are present in different clusters. Mostly, there is one NameNode, so after crashing or termination, the system may be unavailable.
- Which Python libraries would you utilise for proficient data processing?
It helps the recruiter to assess whether the data engineer is well versed with Python or not. Whatever your answer is, don't forget to include NumPy. NumPy is used for processing arrays efficiently. These arrays could have numbers. Also include Pandas, which have great statistical tools and help to prepare data for machine learning.
- What is the difference between Lists and Tuples?
Data structures have classes called Lists and Tuples. Lists can be edited, hence are mutable. But Tuples, on the other hand, cannot be changed and are hence immutable. Give examples if you want to.
- What are the names of the XML configuration files in Hadoop
The XML configuration files that are available in Hadoop are given below:
- What is FSCK?
File System Check, which is also known as FSCK, is an important command of the HDFS. When you require checking of file errors and discrepancies, then it is useful.
- In HDFS, what are Block and Block Scanner?
When Hadoop sees a large file, it divides the file automatically into minor chunks called blocks which are the minimum data entity. Now, a block scanner ensures that the loss of blocked dues to Hadoop are installed on the DataNode without any interruptions.
- What is COSHH?
COSHH is an acronym for Classification and Optimisation-based Scheduling for Heterogeneous Hadoop Systems. It helps to schedule both the cluster and application levels to reduce the completion time of an assignment.
- How is Hive used in the Hadoop ecosystem?
Hive is used for providing a user interface, which helps to manage all of Hadoop's stored data. HBase is used to map and work on data as required. Hive queries help to synthesise MapReduce jobs. Moreover, it helps in the efficient management of the ecosystem when multiple jobs are being run at the same time.
- What is Rack Awareness?
Rack Awareness is used by NameNode, which further uses the DataNode for boosting incoming network traffic while running reading or writing operations simultaneously. It is performed on the file nearest to the rack from which the request is being made.
- What tools did you use in a recent project?
This question is asked to assess your decision-making skills and knowledge about different data engineering tools. This question is for experienced data engineers only and rarely put to freshers. But if you have ever handled a data engineering project, then you should be able to answer this question. Some examples are given below:
- Big Query
- Apache Spark
- Apache Hive
You should be able to point out the tool's strengths and weaknesses to show you know how things work.
- Name the two messages that NameNode obtains from DataNode
NameNodes gets data from DataNodes in the form of signals or messages. The two signals are:
- Block report signals on DataNode. These are related to lists of data blocks and their functioning.
- Heartbeat signals to show that the DataNode is functioning and alive and functional. These signals send periodic reports to find out and assess whether to use NameNode or not.
- How can you deploy a big data solution?
There are important steps for deploying a big data solution –
- Extracting data from data sources like Salesforce, RDBMS, MySQL or SAP.
- Storing the extracted data in a NoSQL or HDFS database.
- Processing frameworks like Pig, Spark and MapReduce
- How are duplicate data points dealt with in an SQL query?
Interviewers assess your SQL knowledge with this question. Suggest using SQL keywords like DISTINCT and UNIQUE for reducing duplicate data points. GROUP BY can also be used for dealing with duplicate data points.
- How can data analytics help the business grow and boost revenue?
Data analytics help the business to grow and generate revenue. Big Data analysis helps boost revenue, improve the customer experience, and increase profits. Data analytics helps to set realistic goals, hence helping in decision-making. Big Data analytics helps to grow 5%-20% of the revenue. Walmart, Facebook and LinkedIn are companies using big data analytics to grow their revenue.
- Do you possess any experience in building data systems using the Hadoop framework?
If you have used Hadoop before, answer by explaining in detail the project using your skills and how well you were able to use the framework. Explain all the important characteristics of Hadoop, and don't forget to mention its scalability and ability and speed of processing data while preserving the data quality.
Some features of Hadoop are as follows:
- Since it is based on Java, it is easy to learn and use.
- All the data storage is done within Hadoop. You can access the data in case the hardware fails at any instance
- In Hadoop, data storage is done in cluster form, and it is hence, independent of other operations.
- What challenges came up during your recent project, and how did you overcome these challenges?
All employers want to understand your difficulty handling prowess and how you face challenges. You can frame your reply by employing the STAR method:
- Situation: Narrate in brief how the problem happened.
- Task: Tell them how you overcame the problem. This speaks a lot about your inborn leadership abilities.
- Action: What steps did you take to fix the problem?
- Result: What were the consequences of the actions you undertook? What did you and the other stakeholders learn about the situation?
These are some of the questions that will certainly help you prepare for your data engineering interviews better. Now, a point to remember here is that there will be other technical questions barring the ones mentioned above, so prepare accordingly. Also, there will be other questions regarding your personal life and your soft skills, so make a point to revise them as well.
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