List of Most Frequently Asked Data Engineer Interview Questions And Answers to Help You Prepare For The Upcoming Interview:
Today, data engineering is the most sought after field after software development and it has become one of the fastest-growing job options in the world. Interviewers want the best data engineers for their team and that’s why they tend to interview the candidates thoroughly. They look for certain skills and knowledge. So, you have to be prepared accordingly to meet their expectations.
What You Will Learn:
Responsibilities Of A Data Engineer
The responsibilities include:
- To handle and supervise the data within the company.
- Maintain and handle the data’s source system and staging areas.
- Simplify data cleansing along with subsequent building and improving the reduplication of data.
- Make available and execute both data transformation and ETL process.
- Extracting and doing ad-hoc data query building.
Skills Of A Data Engineer
With qualifications, you need certain skills as well. They both are crucial when you are preparing for the position of a data engineer. Here, we are listing the top 5 skills, in no particular order, that you will need to become a successful data engineer.
- Skills in data visualization.
- Python and SQL.
- Data modeling knowledge for both Big Data and Data Warehousing
- Mathematics
- Know-how in ETL
- Big Data space experience
So, you must work on improving these skill sets before you start preparing for your interview. And when you have polished your skills, here are some interview questions you can prepare to make the interviewers take notice of you and hire you as well.
Frequently Asked Data Engineer Interview Questions
General Interview Questions
Q #1) Why did you study data engineering?
Answer: This question aims to learn about your education, work experience, and background. It might have been a natural choice in the continuation of your Information Systems or Computer Science degree. Or, maybe you have worked in a similar field, or you might be transitioning from an entirely different work area.
Whatever your story may be, don’t hold back or shy away. And while you are sharing, keep highlighting the skills that you have learned along the way and the excellent work you have done.
However, don’t start storytelling. Start with your educational background a little and then reach to the part when you knew you wanted to be a data engineer. And then move on how you reach here.
Q #2) What is the toughest thing about being a data engineer according to you?
Answer: You must answer this question honestly. Not every aspect of all the jobs is easy and your interviewer knows that. The aim of this question is not to pinpoint your weakness but to know how you work through things you find difficult to deal with.
You can say something like, “As a data engineer I find it hard to complete the request of all the departments in a company where most of them often come up with conflicting demands. So, I often find it challenging to balance them accordingly.
But it has offered me a valuable insight into the workings of the departments and the role they play in the overall company’s structure.” And this is just one example. You can and should put your point of view.
Q #3) Tell us an incident where you were supposed to bring data together from various sources but faced unexpected issues and how did you resolve it?
Answer: This question is an opportunity for you to demonstrate your problem-solving skills and how you adapt to the sudden plan changes. The question could be addressed generally or specifically with context to data engineering. If you haven’t been through such an experience you can deliver a hypothetical answer.
Here is a sample answer: “In my previous franchise company, I and my team were supposed to collect data from various locations and systems. But one of the franchises changed their system without giving us any prior notice. This resulted in a handful of issues for data collection and processing.
To resolve that, we had to come up with a quick short-term solution first for getting the essential data into the company’s system. And after that, we have developed a long-term solution to prevent such issues from happening again.”
Q #4) How is the job of a data engineer different from that of a data architect?
Answer: This question is meant to check if you understand that there are differences within the team of a data warehouse. You can’t go wrong with the answer. The responsibilities of both of them overlap or vary depending on what the database maintenance department or the company needs.
You can say that “according to my experience, the difference between the roles of a data engineer and a data architect varies from company to company. Although they work very closely together, there are differences in their general responsibilities.
Managing the servers and building the architecture of the data system of a company is the responsibility of a data architect. And the work of a data engineer is to test and maintain that architecture. Along with that, we, data engineers, make sure that the data that is made available to the analysts is of high quality and reliable.”
Technical Interview Questions
Q #5) What are Big Data’s four V’s?
[image source]
Answer:
The four V’s of Big Data are:
- The first V is Velocity which is referred to the rate at which Big Data is being generated over time. So, it can be considered as analyzing the data.
- The second V is the Variety of various forms of Big Data, be it within images, log files, media files, and voice recordings.
- The third V is the Volume of the data. It could be in the number of users, the number of tables, size of data, or the number of records.
- The fourth V is Veracity related to the uncertainty or certainty of the data. In other terms, it decides how sure you can be about the accuracy of the data.
Q #6) How is structured data different from unstructured data?
Answer: The below table explain the differences:
| Structured Data | Unstructured Data | |
|---|---|---|
| 1) | It can be stored in MS Access, Oracle, SQL Server, and other similar traditional database systems. | It can’t be stored in a traditional database system. |
| 2) | It can be stored within different columns and rows. | It can’t be stored in rows and columns. |
| 3) | An example of structured data is online application transactions. | Examples of unstructured data are Tweets, Google searches, Facebook likes, etc. |
| 4) | It can be easily defined within the data model. | It can’t be defined according to the data model. |
| 5) | It comes with a fixed size and contents. | It comes in various sizes and contents. |
Q #7) Which ETL tools are you familiar with?
Answer: Name all the ETL tools you have worked with. You can say, “ I have worked with SAS Data management, IBM Infosphere, and SAP Data Services. But my preferred one is PowerCenter from Informatica. It is efficient, has an extremely high-performance rate, and is flexible. In short, it has all the important properties of a good ETL tool.
They smoothly run business data operations and guarantee data access even when there are changes taking place in business or its structure.” Make sure you only talk about the ones you have worked with and the ones you like working with. Or, it could tank your interview later.
Q #8) Tell us about design schemas of data modeling.
Answer: Data modeling comes with two types of design schemas.
They are explained as follows:
- The first one is the Star schema, which is divided into two parts- the fact table and the dimension table. Here, both the tables are connected. Star schema is the simplest data mart schema style and is most widely approached as well. It is named so because its structure resembles a star.
- The second one is the Snowflake schema which is the extension of the star schema. It adds additional dimensions and is called a snowflake because its structure resembles that of a snowflake.
Q #9) What is the difference between Star schema and Snowflake schema?
[image source]
Answer: The below table explain the differences:
| Star Schema | Snowflake Schema | |
|---|---|---|
| 1) | The dimension table contains the hierarchies for the dimensions. | There are separate tables for hierarchies. |
| 2) | Here dimension tables surround a fact table. | Dimension tables surround a fact table and then they are further surrounded by dimension tables. |
| 3) | A fact table and any dimension table are connected by just a single join. | To fetch the data, it requires many joins. |
| 4) | It comes with a simple DB design. | It has a complex DB design. |
| 5) | Works well even with denormalized queries and data structures. | Works only with the normalized data structure. |
| 6) | Data redundancy- high. | Data redundancy- very low. |
| 7) | Aggregated data is contained in a single dimension. | Data is split into different dimension tables. |
| 8) | Faster cube processing. | Complex join slows cube processing. |
Q #10) What is the difference between Data warehouse and Operational database?
Answer: The below table explain the differences:
| Data Warehouse | Operational Database | |
|---|---|---|
| 1) | These are designed to support the analytical processing of high-volume. | These support transaction processing of high-volume. |
| 2) | Historical data affects a data warehouse. | Current data affects the operational database. |
| 3) | New, non-volatile data is added regularly but remains rarely changed. | Data is updated regularly as the need arises. |
| 4) | It is designed for analyzing business measures by attributes, subject areas, and categories. | It is designed for real-time processing and business-dealings. |
| 5) | Optimized for heavy loads and complex queries accessing many rows at every table. | Optimized for a simple single set of transactions like retrieving and adding one row at a time for every table. |
| 6) | It is full of valid and consistent information and doesn’t need any real-time validation. | Improved for validating incoming information and uses validation data tables. |
| 7) | Supports a handful of OLTP like concurrent clients. | Supports many concurrent clients. |
| 8) | Its systems are mainly subject-oriented. | Its systems are mainly process-oriented. |
| 9) | Data out. | Data In. |
| 10) | A huge number of data can be accessed. | A limited number of data can be accessed. |
| 11) | Created for OLAP, on-line Analytical Processing. | Created for OLTP, on-line transaction Processing. |
Q #11) Point out the difference between OLTP and OLAP.
Answer: The below table explain the differences:
| OLTP | OLAP | |
|---|---|---|
| 1) | Used to manage operational data. | Used to manage informational data. |
| 2) | Clients, clerks and IT professionals use it. | Managers, analysts, executives, and other knowledge workers use it. |
| 3) | It is customer-oriented. | It is market-oriented. |
| 4) | It manages current data, the ones that are extremely detailed and are used for decision making. | It manages a huge amount of historical data. It also provides facilities for aggregation and summarization along with managing and storing data at different levels of granularity. Hence the data becomes more comfortable to be used in decision making. |
| 5) | It has a 100 MB-GB database size. | It has a 100 GB-TB database size. |
| 6) | It uses an ER (entity-relationship) data model along with a database design that is application-oriented. | OLAP uses either a snowflake or star model along with a database design that is subject-oriented. |
| 7) | The volume of data is not very large. | It has a large volume of data. |
| 8) | Access mode- Read/Write. | The access mode is mostly write. |
| 9) | Completely normalized. | Partially normalized. |
| 10) | Its processing speed is very fast. | Its processing speed depends on the number of files it contains, complex queries, and batch data refresh |
Q #12) Explain the main concept behind the Framework of Apache Hadoop.
Answer: It is based on the MapReduce algorithm. In this algorithm, to process a huge data set, Map and Reduce operations are used. Map, filters and sorts the data while Reduce, summarizes the data. Scalability and fault tolerance are the key points in this concept. We can achieve these features in Apache Hadoop by efficiently implementing MapReduce and Multi-threading.
Q #13) Have you ever worked with Hadoop Framework?
[image source]
Answer: Many hiring managers ask about the Hadoop tool in the interview to know if you are familiar with the tools and languages the company uses. If you have worked with the Hadoop Framework, tell them the details of your project to bring in light about your knowledge and skills with the tool and its capabilities. And if you haven’t ever worked with it, some research to show some familiarity with its attributes will also work.
You can say, for example, “While working on a team project, I have had the chance to work with Hadoop. We were focused on increasing the efficiency of data processing, so, due to its ability to increase the speed of data processing without compromising the quality during its distributed processing, we decided to use Hadoop.
And as my previous company expected a considerable increase in data processing over the next few months, its scalability came in handy as well. Hadoop is also an open-source network based on Java, that makes it the best option for the projects with limited resources and an easy one to use without any additional training.”
Q #14) Mention some important features of Hadoop.
Answer: Features are as follows:
- Hadoop is a free open source framework where we can alter the source code as per our requirement.
- It supports the faster-distributed processing of data. HDFS Hadoop stores data in a distributed manner and uses MapReduce to parallel process the data.
- Hadoop is highly tolerant and by default, at different nodes, it allows the user to create three replicas of each block. So, if one of the nodes is unsuccessful, we can recover the data from another node.
- It is also scalable and is compatible with many hardware.
- Since Hadoop stored data in clusters, independent of all the other operations. Hence it is reliable. The stored data remains unaffected by the malfunctioning of the machines. And so, it is highly available as well.
Q #15) How can you increase the business revenue by analyzing Big Data?
Answer: Big data analysis is a vital part of the businesses since it helps them to differentiate from one another along with increasing the revenue. Big data analytics offers customized suggestions and recommendations to businesses through predictive analysis.
It also helps businesses in launching new products based on the preferences and needs of the customers. This helps the businesses earn significantly more, approximately 5-20% more. Companies like Bank of America, LinkedIn, Twitter, Walmart, Facebook, etc. use Big Data Analysis to increase their revenue.
Q #16) While deploying a Big Data solution, what steps you must follow?
Answer: There are three steps to be followed while deploying a Big Data solution:
- Data Ingestion- It is the first step in deploying a Big Data solution. It is the extraction of the data from various sources like SAP, MYSQL, Salesforce, log files, internal database, etc. Data ingestion can happen through real-time streaming or batch jobs.
- Data Storage- After the data is ingested, the extracted data should be stored somewhere. It is either stored in HDFS or NoSQL databases. HDFS works well for sequential access through HBase for random read or writes access.
- Data Processing- This is the third and the concluding step for deploying on a Big Data solution. After storage, the data is processed through one of the main frameworks like MapReduce or Pig.
Q #17) What is a block and block scanner in HDFS?
Answer: A block is the minimum amount of data that can be written or read in HDFS. 64MB is the default size of a block.
The block scanner is a program that tracks the number of blocks on a DataNode periodically along with verifying them for any possible checksum errors and data corruption.
Q #18) What are the challenges you have faced while introducing new data analytics applications if you have ever introduced one?
Answer: If you have never introduced new data analytics, you can simply say so. Because they are quite expensive and hence it is not often that companies do that. But if a company decides to invest in it, it can be an extremely ambitious project. It would need highly trained employees to install, connect, use, and maintain these tools.
So, if you have ever been through the process, tell them what obstacles you faced and how you overcame them. If you haven’t, tell them in detail what you know about the process. This question determines if you have the basic know-how to get through the problems that might arise during the introduction of new data analytics applications.
Sample Answer; “I have been a part of introducing new data analytics in my previous company. The entire process is elaborate and needs a well-planned process for a smoothest possible transition.
However, even with immaculate planning, we can’t always avoid unforeseen circumstances and issues. One such issue was an incredibly high demand for user licenses. It went over and beyond what we expected. For obtaining the additional licenses, the company had to reallocate the financial resources.
Also, training had to be planned in a way that it doesn’t hamper the workflow. Also, we had to optimize the infrastructure to support the high number of users.”
Q #19) What if NameNode crashes in the HDFS cluster?
Answer: The HDFS cluster only has one NameNode and it maintains DataNode’s metadata. Having only one NameNode gives HDFS clusters a single point of failure.
So, if NameNode crashes, systems might become unavailable. To prevent that, we can specify a secondary NameNode that takes the periodic checkpoints in HDFS file systems but it is not a backup of the NameNode. But we can use it to recreate NameNode and restart.
Q #20) Difference between NAS and DAS in the Hadoop Cluster.
Answer: In NAS, storage and compute layers are separate, and then storage is distributed among various servers on the network. While in DAS, storage is usually attached to the computation node. Apache Hadoop is based on the principle of processing near a specific data location.
Hence, the storage disk should be local to computation. DAS helps you get performance on a Hadoop cluster and may be used on commodity hardware. In simple words, it is more cost-effective. NAS storage is preferred with high bandwidth of around 10 GbE.
Q #21) Is building a NoSQL database better than building a relational database?
[image source]
Answer: In answer to this question, you must showcase your knowledge about both the databases. Also, you must back it up with an example of the situation demonstrating how you will or have applied the know-how in a real project.
Your answer could be something like this “ In some situations, it might be beneficial to build a NoSQL database. In my last company when the franchise system was exponentially increasing in size, we had to scale up quickly for making the most of all operational and sales data we had.
Scaling out is better than scaling up with bigger servers when handling the increased data processing load. It is cost-effective and easier to accomplish with NoSQL databases as it can easily deal with huge volumes of data. That comes in handy when you need to respond quickly to considerable data load shifts in the future.
Although relational databases come with better connectivity to any analytics tools. But NoSQL databases have a lot to offer.”
Q #22) What do you do when you encounter an unexpected problem with data maintenance? Have you tried any out-of-the-box solutions for that?
Answer: Inevitably, unexpected issues arise every once in a while in every routine task, even while data maintenance. This question aims to know if you can deal with high-pressure situations and how.
You can say something like “ data maintenance might be a routine task but it is vital to closely watch the specific tasks, including making sure of successful execution of the scripts.
Once while conducting the integrity check, I came across a corrupt index that could have caused serious issues in the future. That’s why I came up with a new maintenance task for preventing the addition of corrupt indexes into the database of the company.”
Q #23) Have you ever trained someone in your field? If yes, what have you found most challenging about it?
Answer: Usually data engineers are needed to train their coworkers on new systems or processes that you have created or train new employees on already existing systems and architecture. So, with this question, your interviewer wants to know if you can handle that. If you haven’t had the chance to train someone yourself, talk about the challenges someone who trained or you know you faced.
A sample of the ideal answer will be something like this. “ Yes, I have had the chance to train small and large both groups of co-workers. Training new employees with significant experience in another company is the most challenging task I have come across. They are often so used to approaching data from one different perspective that they struggle to accept the way we do things.
Often, they are extremely opinionated and think they know everything right and that’s why it takes a lot of time for them to realize that a problem can have more than one solution. I try to encourage them to open their minds and accept alternate possibilities by emphasizing on how successful our architecture and processes have been.”
Q #24) What are the pros and cons of working in cloud computing?
[image source]
Answer:
Pros:
- No infrastructure cost.
- Minimum management.
- No hassles regarding management and administration.
- Easy to access.
- Pay for what you use.
- It is reliable.
- It offers data control, backup, and recovery.
- Huge storage.
Cons:
- It needs a good internet connection with equally good bandwidth to function well.
- It has its downtime.
- Your control of infrastructure will be limited.
- There is little flexibility.
- It has certain ongoing costs.
- There might be security and technical issues.
Q #25)The work of data engineers is usually ‘backstage’. Are you comfortable working away from the ‘spotlight’?
Answer: Your hiring manager wants to know if you love limelight or you can work well in both situations. Your answer should tell them that although you do like the limelight, you are comfortable working in the background as well.
“ What matters to me is that I should be an expert in my field and contribute to my company’s growth. If I have to work in the spotlight, I am comfortable doing that as well. If there is an issue that executives need to address, I will not hesitate in raising my voice and bringing it to their attention.”
Q #26) What happens when the Block scanner detects a corrupt data block?
Answer: First of all DataNode reports to NameNode. Then NameNode starts creating a new replica through the replica of the corrupt block. Corrupted data block will not be deleted if the replication count of the right replicas matches the replication factor.
Q #27) Have you ever found a new innovative use for already existing data? Did it affect the company positively?
Answer: This question is meant for them to find out if you are self-motivated and eager enough to contribute to the success of the projects. If possible, answer the question with an example where you took the charge of a project or came up with an idea. And if you ever presented a novel solution to a problem, don’t miss it either.
Example answer: “ In my last job, I took part in finding out why we have a high employee turnover rate. I observed the data closely from various departments where I found highly correlated data in key areas like finance, marketing, operations, etc. and the rate of employee turnover.
Collaborated with the department analysts for a better understanding of those correlations. With our understanding, we made some strategic changes that affected the employee turnover rate positively.”
Q #28) What non-technical skills do you think comes in most handy as a data engineer?
Answer: Try to avoid the most obvious answers like communicating or interpersonal skills. You can say, “prioritizing and multitasking have often come in handy in my job. We get various tasks in a day because we work with different departments. And hence, it becomes vital that we prioritize them. It makes our work easy and helps us efficiently finishing them all.”
Q #29) What are some common problems you have faced as a data engineer?
Answer: These are:
- Continuous and real-time integration.
- Storing huge amounts of data and information from those data.
- Constraints of resources.
- Considering which tools to use and which ones can deliver the best results.
Conclusion
Data engineering might sound like a routine boring job but there are many interesting facets to it. That is evident from the possible scenario questions interviewers might ask. You should be ready to answer not just technical bookish questions but also situational questions like the above-listed ones. Only then you will be able to prove that you can do your job well and deserve it.
All the best!!