Data Engineering is one of the fascinating and most preferred fields in the IT industry. This is the reason why the competition for data engineer interviews is also very high. Being prepared with the commonly asked data engineering interview questions is one crucial step that will keep you ahead in the competition.
With diversity increasing opportunities for a data engineer, the data engineering interview questions asked are also turning out to be selective and role-specific. Tools related to data engineering and data modeling interview questions are primarily concentrated when applying with less experience. When more experienced and are used for roles like a data center manager, commonly asked manager interview questions, etc., are to be concentrated on.
Read below to know the various categories of data engineering interview questions that interviewers usually focus on.
General Interview Questions
Every interviewer in the IT industry would generally like to start with at least one personal question. That could be anything from trying to know about you to testing your knowledge about the company that you are being interviewed for. While it is 100% necessary to concentrate on technical content, spending a fair share of time on these communication and personality testing questions is also equally important.
1. Tell me about yourself
Do not go for your personal details that are already present in the resume. Instead, finish that part in 2 or 3 lines, and then speak about your technical knowledge. Since you are attending the interview for the role of a data engineer, mention the related concepts, tools, and software about which you have an idea and certifications. Also, talk briefly about your previous work, your experience, the companies you worked for, the projects you were involved in, and the clients you have dealt with.
2. Why did you choose data engineering as a career path?
This seems like a question to not much concentrate on, but remember, this is one of the very commonly asked data engineering interview questions in almost all the interviews. Your response to this will give an idea to the interviewer about how passionate you are about the job. First, start briefly talking about which field you have started your career in, and then clearly explain what has driven you to get into data engineering. Next, say how invested you are in this field by talking about the list of all the skills you have step by step learned to gain knowledge and experience in data engineering.
3. Why should we select you for this role?
This is another question that requires you to stress your skills, certifications, and experience. Talk about all the highlights in your career so as to give an idea about how resourceful you are. Also, talk about your personality, about primary features like confidence, teamwork, leadership, etc., and also secondary features like disciplined, hardworking, friendly, etc. Do some good research about the company you are attending the interview for, and finally, talk about how your skills, experience, and personality would suit the company’s objectives. Remember, your answer should be subtle but not at all boastful.
Data Engineering Interview Questions for freshers
No matter how much of a pro you are in the core topics, introduction and basic level concepts of Data Engineering are what you are primarily tested in. Answering these questions is what makes the interviewer judge whether you are good at the foundations or not.
4. What is data engineering?
This is one of the basic but fundamental data engineering interview questions. It is highly advisable not to give a textbook answer to this question. Although one or two lines from the standard definition could be said at the start, later go for explaining your point of view about data engineering. Explain on a whole what exactly you think data engineering is and why it is essential.A sample is given below:
Data engineering refers to the particular field in the IT industry that is responsible for transferring all basic data from several sources into a single source for further analysis. The primary data include small data units from sensors or processors to vast amounts of data from a particular cloud or physical storage unit. All this data is brought to a single place by data engineers for processing. All this data is brought to a single place by data engineers for processing. Data engineers hence simply lay the foundation work for data scientists and data analysts to further work with.
5. Can you talk about the role of a data engineer and the skill set required for this role?
Do not repeat the same points that would be included in the general definition of data engineering. Instead, answer this question more technically than generally. This gives the interviewer the feeling that you are quite passionate about this role and have invested a lot of time learning and gaining experience.
Data engineers are involved in several tasks, and these tasks depend on the role they are working for. But whatever the position is, the essential skills that every data engineer should have an idea about are:
- Complete hands-on experience and in-depth knowledge about SQL and NoSQL.
- Working understanding of data modeling.
- Step by step idea about the working of Extract, Transform and Load (ETL) processing.
- Theoretical understanding of data pipelining.
- Programming and coding skills in Hadoop (HDFS).
- Conceptual knowledge about big data, metadata, and data warehouses.
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6. Tell me a few things about structured and unstructured data.
Every data engineer works with 3 kinds of data, namely structured, unstructured and semi-structured data.
Structured data is a basic format of data that consists of only numbers and values. It is the oldest format and can be easily stored in tables through DBMS. This data can easily be stored in data warehouses through Extract, Transform, and Load (ETL) and data pipelining.
Unstructured data is a more unspecific form that includes everything from jpeg images to mp4 audio formats. They do not coordinate with ETL processing and instead have to be entered manually through a code. The scaling for unstructured data is easy and the data can be stored in data lakes instead of data warehouses.
7. What are the top tools and software used by data engineers?
Tools:
- SQL & NoSQL – used for creating data structures, implementing business logic, etc.
- PostgreSQL – for creating more effective and robust databases and data structures
- MongoDB – for easy adaptable and customized NoSQL databases
- Apache Spark – for building faster and more reliable data pipelines
- Apache Airflow – to build better frameworks to run for efficiently running complex data pipelines
- Apache Kafka – to get high-performance data pipelines through a high-throughput and low-latency platform
- Amazon Redshift – for cost-effective data warehouses that work faster with less complexity
- Amazon Athena – for creating data lakes that can manage both structured and unstructured data
- Snowflake – for managing infrastructure in the most simplified procedures
Software:
- Python – commonly used language in most data engineering applications
- Java – robust language with high execution speeds when dealing with Big Data
- Scala – commonly used language during data modeling and for building data pipelines
Data Modeling Interview Questions
The answer to every question related to data modeling is assessed carefully as they are considered to be very important data engineering interview questions. Furthermore, Data modeling plays a crucial role in all businesses as it involves dealing with several clients and their requirements. Hence, have a solid amount of knowledge on this topic to answer the questions more effectively.
8. What is Data Modelling?
Data modeling is the process of creating a model that represents how data is stored in a database. Any data stored in a cloud or a data warehouse is usually raw data that consists of structured and structured formats of text, values, images, etc. Data modeling provides the complete information about these several data types used and also shows us the relation between every data unit present in the storage. Data modeling makes further data analysis more accessible, helps you to quickly identify errors, and also reduces the complexity to a great level.
Also, mention points about your own experience in working with data modeling.
9. Name the different types of data models.
There are 3 different types of data models – conceptual, logical, and physical.
- Conceptual: They are essential domain models that show the overall groundwork of the model by gathering the initial requirements of that particular project. Details about all the structured and unstructured data that the system will contain and all the relevant business rules to be followed in these classes are all established in the conceptual type data models.
- Logical: These data models provide in-depth detail about the concepts used rules followed in a particular project. They also explain the relations between the several data units used. This extensive detail is unnecessary for small projects but is very useful when dealing with large amounts of data in warehouses or data lakes.
- Physical: This is the final step in providing a model view for creating a particular database. It has every single detail necessary for the engineers to design the relational database.
10. Do you know the different schemas available in Data Modelling?
2 types of schemas are commonly used in data modeling – snowflake schema and star schema.
- Star Schema – A single fact table or a combined set of fact tables reference several dimensional tables arranged around the fact table, thereby making it look like a star.
- Snowflake – A single fact table or a combined set of fact tables reference several dimensional tables arranged in multiple dimensions. This means the dimensional tables that are referenced by fact tables are further broken down to reference following dimensional tables, and the process continues.
Apart from these basic definitions, try mentioning what fact tables and dimensions are. Also, explain about one or two applications regarding both star and snowflake schemas. Finally, if possible, mention your personal experience working with these during the process of data modeling.
Big Data & Hadoop-Related Data Engineering Interview Questions
Every major large IT firm is now dealing with Big Data and Hadoop is undoubtedly an integral part of Data Engineering. Do not hence miss mastering all the basics of the Hadoop tool and learning theoretical concepts related to Big Data.
11. Tell me briefly about Big Data.
Data that is present in enormous volumes in so many different formats is known as Big Data. The higher speed of data transformation and the greater complexity of the data is what makes it Big Data. This is because ordinary data management tools fail to handle the huge sets of information that come under Big Data. Apache Storm, MongoDB, etc. are some of the commonly used tools that support Big Data. With these tools, we can extract, transfer, and analyze all the data that comes under Big Data.
12. How is Big Data Related to Hadoop?
Apache Hadoop is one of the best tools that help us in handling and managing Big Data. The MapReduce feature in Hadoop gives this software tool the capabilities to program on such large amounts of data present in Big Data. In simpler terms, Hadoop helps us transform all the heavy volumes of raw data present in Big Data into useful information to analyze and further take action.
13. How is Hadoop used in Data Engineering?
Hadoop is one of the most popular data engineering tools that are used by almost all IT businesses. It has become that common in data engineering primarily because it is capable of dealing with Big Data. In addition, several software utilities like Hadoop Map Reduce, Hadoop HDFS, etc. are capable of gathering and carefully splitting the data into several sections for data analysis.
Also, mention your experience with Hadoop and about where you stand in theoretical and practical knowledge about the different tools provided by Apache Hadoop.
14. What do you know about Name Nodes in Hadoop?
Name Node is a central node in Hadoop HDFS where metadata is stored. All details regarding the cluster of data, such as the location where the data is stored, the format of the data, its size, etc. are all stored in the Name Node as metadata. They play a significant part because data analysis becomes very difficult and complex without metadata, which delays processing. This is why Name Nodes are handled with care, and if possible, backup Name Nodes are also stored in the header files to call by default when there are any issues with the original Name Node.
15. What is HDFS?
Hadoop Distributed File System (HDFS) is one of the software utilities in Hadoop that deal with storing large volumes of data. HDFS helps to easily store Big Data in hardware by dividing the entire data into smaller parts and storing each part in a different storage unit. Name Node and Data Node are the 2 primary files in HDFS.
Apart from this, add details about your experience with HDFS and also the importance of HDFS in today’s data engineering and data analysis.
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16. What does a block constitute in HDFS? How is Block Scanner useful?
All the data handled through Hadoop HDFS is split into several small parts, each known as a block. Every block contains a small amount of huge data that can be individually analyzed. The metadata regarding how many blocks the data is divided into, where each of those is located, etc., is all stored in the Name Node.
Block Scanner is very useful in HDFS as it verifies all blocks and tells us whether all the blocks are fine or if any block has got corrupted. The block node scans through all the data inside a block and identifies corrupt data nodes. This saves a lot of time during transmission to quickly monitor which block of data is causing the issue.
Data Centre Manager Interview Questions
The architectural, organizational, and managerial parts of Data Engineering are some of the key concepts included in data engineering interview questions.
17. Do you know what a data pipeline is?
A data pipeline is a serial interlink of many tools and processes to transfer data from one storage to another. A pipeline should send data from several sources, like physical storage units, cloud storage, etc., to a single cloud server or data warehouse where the data further goes through analytical processing. Constructing robust data pipelines while ensuring there is no data loss or data override is one of the primary tasks of a data engineer.
18. What do you know about data warehouses?
A data warehouse (DW) is a large system where all the large volumes of data of a particular company or business are collected and stored. While the data engineers transfer the content into these data warehouses, the data scientists and data analytic engineers then further work with it. The data in DWs can be:
- Structured
- Unstructured
- Semi-structured
Older data warehouses can manage only structured data that can be filled into a single table or a set of tables. But today’s modern DWs can handle all sorts of data formats like jpg images, pdf files, mp4 audios, and many more.
19. Can you elaborate on the term ETL?
ETL consists of the key processes in data engineering. The ETL acronym stands for Extract, Transform and Load which are the 3 basic operations computed by data center management.
- Extract deals with collecting all the data present in the company’s cloud storage, physical storage units, servers, etc., and transferring all that data through a data pipeline.
- Transform deals with converting all the raw data extracted into an organized format, to reduce complexity in the work of data scientists and analysts.
- Load deals with sending all the extracted and transformed data into a target, which is a different database or a data warehouse.
20. Tell a few operations involved in ETL testing.
ETL testing plays a very crucial role during the entire process of Extract, Transform and Load. The ETL testing team has numerous tasks to perform, which can all be categorized into 4 important operations. They are:
- Constant monitoring of whether or not all business requirements are met and satisfied during the “transform” process of ETL.
- Protecting the system from data loss during the transformation from one cloud to another cloud or data warehouse.
- Ensuring that every false data is notified and necessary replacements with default data immediately take place.
- Managing the data pipeline process to be robust, operating in a minimum time frame.
21. What is metadata?
Metadata is “data about data”. Metadata is the term used for data containing all relevant information required about any particular form of data we are working with. After the ETL and data pipeline process, data from several sources are all loaded into a single cloud or a data warehouse. It will be highly complex and messy if we know the origins of every data unit in the warehouse. This is why a header file is attached to every data unit containing the details about the particular unit. Details usually included in metadata are:
- Origin location of the data and the table containing that data
- Format of the data
- Who created the table containing that data?
- Date and time of when the table is created and when it is last updated?
- Purpose of the table
- Meaning of certain short forms and abbreviations used in the table
Questions about Previous Work Done
This is the last section under the most important data engineering interview questions. Data Engineering is one of the hard-core fields in the IT industry and every tool and concept used here requires skill and practice to handle. This is the reason why the interviewer would like to know about the biggest challenges you faced, issues you handled, or algorithms you worked within this data engineering field. This gives an idea to the interviewer about how experienced you are, which in turn decides how capable you are for this job.
Hence, make sure to brush the details about all the previous projects that you worked on.
Conclusion
If you are new to this field, make sure to study all the data mentioned above engineering interview questions in-depth. If you are going through the interview with experience, it is necessary to go through all the previous projects done along with all the different kinds of tools and algorithms used in those projects. Also, when applying for a company, make sure to enquire what position they are looking for. It is sufficient to be well-versed with the basics of all concepts for generic data engineer roles. Still, when the requirement is for specific roles like a data center manager, it is essential to spend extra hours studying that field.
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