Describe a time when you found a new use for existing data that had a positive impact on the business.

"As a Data Engineer, I try to take time to understand the strategic initiatives being conducted across the company. I believe departments need to avoid working in silos and should have approved access to data owned by other groups within the company. From my perspective as a Data Engineer, I was able to connect employee data with sales data to better understand the reasons behind both high and low sales periods. Upon further analysis, it was revealed that hiring employees with a particular education and work experience profile resulted in significant increases in sales for an extended period of time. Prior to this discovery, human resource data was never used in conjunction with sales data for analyses."

As a Data Engineer, you may be one of the few who have a bird's eye view of the data throughout a company. In many situations, departments work with a specific set of tables within the corporate databases and therefore may unknowingly be limiting their analyses. To add the most value to the company's strategies, it is valuable, at a general level, to know the initiatives of each department. This gives Data Engineers the ability to provide valuable insight into what data is available and beneficial for analyses being conducted throughout the company.

What do you find to be the most difficult aspect of being a Data Engineer?

"One difficult aspect of being an Data Engineer is managing the sometimes conflicting demands of different departments within the company. Balancing the needs of the different departments with the capabilities of our infrastructure is one the biggest challenges I deal with on a regular basis. Although it has been difficult, I always try to see the positive aspect of the situation. Dealing with these conflicting demands has required me to learn more about the work of all of these departments. It gives me an invaluable holistic view of the company and allows me to see how all the 'pieces' fit together. So, in a way, I feel fortunate to have this challenge as there are only a few others who are exposed to this view of the company."

Reasonable hiring managers will understand that people run across difficult aspects of their job all the time. Avoid glossing over this question in fear of highlighting a weakness. Instead identify something you have may have struggled with and add how you dealt with it.

What led you to a career in Data Engineering?

"As long as I can remember, I have always had an interest in computers. Once I reached high school, I knew I wanted to pursue a degree in Computer Engineering. While in college, I began to realize that I enjoyed my math and statistics courses almost as much as my computer courses. Upon graduation, my first job was a Data Analyst position for a large financial services company. I found great satisfaction in using my math and statistical skills, but missed using more of my programming and data management skills. Through some associates in my company, I learned about the Data Engineering field and started taking courses to learn more about it. I found it to be the perfect combination of my interests and skills. Fortunately, within a year, a Data Engineering position opened up in my company and I was able to make the switch without a problem."

Some arrived to the Data Engineering field along a very traditional path - earning a degree in a related area (Computer Science, Information Systems, Data Science, etc.) and working at data-related jobs along the way. Others may have started on an entirely unrelated career path and made the switch to Data Engineering. Either way, the answer to this question reveals more about your education and experiences and the decisions you made along the way. Do not be hesitant to share your background and experiences if you did not arrive to this field the traditional way. Instead, touch upon what general skills you may have attained while earning your degree and working at your other jobs.

If you had the time and resources, what types of training courses would you enroll in to help you in your job as a Data Engineer?

"I find it very important to keep up with advancements in my industry, so I believe enrolling in training courses on a regular basis should be a high priority for Data Engineers. At this time, I would choose to enroll in training courses related to ETL processes and the cloud environment. In current and past roles as a Data Engineer, we are always looking for ways to improve our processes to become more reliable and efficient. I have been fortunate enough to work in teams where our architecture and processes ran relatively smoothly and efficiently. However, it is always important to continuously evaluate your current situation and be proactive about finding ways to improve."

With technology constantly changing, most ambitious Data Engineers could easily rattle off several training courses they would enroll in if they only had the time in their busy schedules. The answer to this question may not only reflect where your interests lie, but it can also be an indication of your perceived weaknesses. Find a way to offset any possible interpretations of weakness by mentioning strengths you have in related skills.

Do you have experience training others on thing such as software, applications, porcesses or architecture? If so, what have you found to be the most difficult aspect of doing so?

"Yes, I do have experience administering both individual and group training. Through my experiences I have found that one of the more difficult aspects is training new, but experience employees, who have come from a company that approached data from an entirely different perspective. These new employees may 'speak the language' and have the necessary skills, but sometimes have strong opinions on how to approach different projects. It becomes a challenge to train them when they struggle to be open-minded. I have learned it is helpful to highlight the successes we've had with our processes and architecture to help them realize there is never a 'one-size-fits-all' solution."

Training may be one of a Data Engineers many responsibilities. Co-workers may need to be trained on new processes or systems you have built or new employees may need training on well established architectures and pipelines. Technology is constantly changing so it is likely training opportunities will exist on a regular basis. When identifying the difficult aspect of training you experienced, be sure to also include how you dealt with it.

Some in the Big Data industry consider Data Engineering to be a non-analytic career path. Do you agree or disagree with that statement and why?

"I would have to disagree with this statement as I have used analytical skills frequently as a Data Engineer. Whether conducting analyses to ensure data quality and integrity or evaluating new service providers or hardware, my analytical skills have been crucial to my performance on the job. In addition, my analytical skills have help me when working with Data Scientists and Analysts on various projects. They help me better understand the data they need for their projects."

There can be a couple of different ways to interpret this statement. Yes, relative to other Big Data career paths, Data Engineers may not use their analytical skills as frequently as a Data Analyst. Therefore, relative to other career paths, Data Engineering may be considered non-analytic. However, this does not mean that Data Engineers do not use analytical skills at all. Hiring managers would like to know how you view a Data Engineer's role versus that of others in the company working with data. Express your understanding of a Data Engineer's role and how analytics is part of the required skill set. Cite at least one example of how you may have used analytics in your past roles as a Data Engineer.

Outside of your technical skills, what skills do you find most valuable as a Data Engineer? These could be skills you attained from jobs unrelated to data.

"Over the years, multitasking and prioritizing have become invaluable skills for me. I began strengthening these skills in a job unrelated to Data Engineering. As an administrative assistant working with a department of a dozen people, I had to learn to prioritize tasks and complete some of the simultaneously. This has become a skill I use frequently as a Data Engineer since I work with many different departments in the company. In any given week, I'm approached by different departments with several different data requests. I have to manage these requests by prioritizing their needs, and in order to get the requests fulfilled efficiently, I use my multi-tasking skills."

Career-specific skills are important to have, but there are many atypical skills that are necessary to be a successful Data Engineer. When answering this question, try to 'think outside the box', and avoid answers such as Communication or Teamwork skills.

Do you prefer work in more of a Generalist role or concentrate your work on the Pipeline or Database?

"With the majority of my work experiences as a Data Engineer, I worked in more of a Generalist role. I prefer this over the other two types, because I enjoy having knowledge of the entire structure and process. I have received training on a variety of topics relevant to Data Engineers and enjoy utilizing all of my attained skills, if possible, instead of concentrating on a subset of them."

Data Engineers may have not have a choice to the type of role they play. Much of it depends on the size and type of company at which they work. Generalists tend to be more highly skilled as they are responsible for a larger variety of data tasks. Those whose work is concentrated with the Pipeline tend to work more closely with Data Scientists and are more familiar with getting the data prepared for analysis. Data Scientists whose work is concentrated on databases may work more with the ETL process and table schemas. As you answer this question, be sure to include all your experiences (if you have worked in more than one type of role) and why you prefer one type over another.

Are you comfortable working 'behind the scenes'? Alternatively, are you comfortable coming out and being in the 'spotlight'?

"As a Data Engineer, I am used to working 'behind the scenes'. What's more important to me than being in the 'spotlight' is being an expert in my field and having a positive impact on the company through my work. I take pride in the work that I do and how I can set the company up for success. However, I do not shy away from the 'spotlight' when necessary. If there is an issue within my department that requires attention from my manager and/or company executives, such as the need for additional resources, I do not avoid bringing attention to it for the good of our team's work and ultimately the company."

Compared to Data Scientists, Data Engineers tend to work 'behind-the-scenes' since their work is completed much earlier in the data analysis project timeline. Decision makers in the company aren't always interested in how the data is made available. Instead, they are usually more interested understanding the learnings Data Scientists glean from the data using their statistical and machine learning models. Your answer to this question will reveal a bit about your personality - whether you only thrive in the 'spotlight' or are you able to work in both types of situations?

Which certifications have you earned that are applicable to your job as a Data Engineer? Which was your most recent one and when did you earn it?

"Over the past few years, I have become IBM Certified as a Data Engineer and also received professional certification through Google. I regularly look for training classes that will broaden my skill set and knowledge and also attend various Big Data conferences throughout the year. I am currently working towards a Microsoft Professional certification in Data Engineering with Azure."

Certifications serve as proof that you received formal training for a skill and not did not just learn it on the job. The number of certifications may also be indicative of your dedication to increasing your knowledge and skill base. Technology in this area is always changing and keeping your skills up to date is vital, so recency of training and certifications could likely be taken into account. If you haven't had the opportunity to work towards any certifications, mention what training you receive on a regular basis to ensure you are up to date on all the technological advancements in your field.

What kind of experience have you had working with Data Scientists and what skills do you have in common with them?

"In most of my positions, I have had the opportunity to work with Data Scientists. I met with many of them on a regular basis to better understand their roles and to aid them with their projects. I found that it was not difficult to work with them, because we both understood analytics and were trained in some of the same programming languages. Having these overlapping skills allowed me to more easily understand the Data Scientist's data needs, while she understood the limitations of our infrastructure and the data available. "

Some working in the industry may think that Data Engineers and Data Scientists have some overlap in skills and possibly responsibilities. At the minimum, Data Engineers should have a general understanding of what type of projects Data Scientists work on. At a high level, the two positions differ in that Data Engineers deal with the maintenance, architecture and overall preparation of data for analytical purposes, while Data Scientist create use statistical and machine learning methods to glean learning from the data. As Data Scientists rely heavily on the work of Data Engineers, hiring managers may want to understand how you have interacted with them in the past and how well you understand their skills and work.

Do you have experience building custom analytics applications? If so, please describe the application you created.

"The Marketing department was looking to marry its primary Marketing Research data with sales data stored in our corporate databases. The custom application I built, allowed them to conduct ad hoc analyses quickly, which in turn helped them react quickly to the competitive environment. The application allowed them to avoid the traditionally long process of requesting data from the data warehouse and loading it into Excel or Access."

Building custom analytics applications requires Data Engineers to not only know the data well, but also to understand the analytic needs of the different groups across the company. They must work closely with these analysts to plan and build these custom applications. So, in addition to having the programming skills, it is important to have good communication and teamwork skills.

Have you ever been in a data disaster recovery situation? If so, describe the situation and your role.

"In my most recent position, I was part of the group charged with developing a Disaster Recovery Plan. Therefore, I was familiar with what needed to take place when a data disaster recovery situation actually occurred. A corrupt file was somehow loaded into our system and caused databases to lock up and much of the data to become corrupted as well. I was responsible for working with our IT team to ensure that our data backups were ready to be loaded and that users throughout the company continued to have connectivity to the data they needed."

Beyond the completion of daily assignments, hiring managers are looking for Data Engineers who can quickly contribute to the remediation of emergency situations. When a data infrastructure fails and/or data becomes inaccessible, lost or destroyed, it can have damaging effects on the company's operations. Companies want to ensure that they are ready with the right resources to deal with these unfortunate events if they occur. In many cases, it becomes an 'all hands on deck' circumstance.

Give an example of when you proposed changes to improve data reliability and quality. Did these changes end up being implemented? If not, why not?

"I believe that the work I produce is only as good as the data or evidence I have to back it up. That's why it is important as a Data Engineer to continuously ensure that the data your company collects and relies on to make strategic decisions is clean and of high quality. If not, I need to help identify the issues and possible solutions to improve the data. As I work on any project, I am continually evaluating whether there is a more efficient or effective process to accomplish the task at hand."

Continuous improvement of the current environment is something hiring managers value. It is even more advantageous for you if these proposed changes were self-initiated and were not an assigned task, so be sure to highlight this if that is the case. This will display your ability to 'think outside the box' and the priority you place on quality of the company's work overall. If you have never had an opportunity to propose changes to improve data reliability and quality, explain why you think it's important and what you would do as a Data Engineer to ensure the data quality was always at a high level. In addition, if your proposed changes were not implemented for reasons such as lack of time or resources, go ahead and state that, but make sure you emphasize your continued commitment to finding new ways to improve data quality.

"While working in previous positions related to data, data quality and reliability were always things I considered as high priorities in my projects. While working on one particular project, I noticed there were several discrepancies and outliers in the data being stored in the company's database. After identifying several examples of this, I built a case to develop a data quality process into our department's routine. This included conducting a weekly meeting with representatives from different departments within the company to identify and troubleshoot data issues. Although this took time away from other projects, everyone felt it was well worth it, and in the long run, saved us from dealing with larger problems that would've been more costly for the company."

"Working as a Data Engineer, I am likely one of the employees most familiar with the company's data. Because of this, I have been able to quickly identify anomalies or issues with the data. However, I recognize that there are many people that work with various data across the company. Therefore, I appreciate the importance of getting input from other departments in the company. So when a data quality issue arose while working at my last position, I reached out to other groups within the company to build a cross-functional team to identify issues and develop a remediation plan. To this day, this group meets on a bi-weekly basis and has broadened its scope to proactively deal with upcoming issues or changes that may affect data reliability or quality."

Data maintenance is one of the many responsibilities of a Data Engineer. In many cases, tasks related to this are fairly routine. Describe a time when you encountered a data maintenance problem that required you to deviate from your "routine".

"As routine as data maintenance may become, it's alway important to keep a close eye on all the tasks involved, including ensuring that scripts are executing successfully. One of the data maintenance tasks involved conducting an integrity check. This allowed us to ensure our systems were not corrupted. After conducting this check, I was able to locate a corrupt index that may have caused larger issues in the future. Because of this discovery, I decided to implement an additional maintenance task as a extra safety precaution to help prevent corrupt indexes from being added to our databases."

Data maintenance usually occurs on a set schedule with a specified task list. With a good data maintenance strategy, these tasks tend not to change often which is a good sign - that means everything is operating as expected. Inevitably, there will be something unexpected that occurs that may throw things off and require extra attention. The interviewer would like to see that you have experience dealing with unexpected situations like these.

Do you have experience with data modeling? If so, what data modeling tools do you have experience using?

"Although I have worked in some companies where I was not highly involved with the data modeling process, I make it a goal to keep myself familiarized with the data models in the company. This always gives me a better understanding of the entire system. However, I have worked at some companies where I was more involved in the data modeling process and had the opportunity to utilize the tools available. I have experience using Oracle SQL Developer Data Modeler which allows us to create, browse and edit a variety of data models, and I found the ability to forward and reverse engineer very helpful as well. This tool helped us develop conceptual models as we work with business stakeholders, and also logical data models where we can define data models, structures and relationships in the database."

As a Data Engineer, you likely have some experience data modeling- defining the data requirements required to support your company's data needs. Besides mentioning the tools you have used for this task, include what you know about data modeling on a general level and possibly what advantages and/or disadvantages you see in using the particular tool(s).

What is your experience level with NoSQL databases? Give me an example of a project/situation where you found building a NoSQL database to be more appropriate than a relational database.

"Modeling a database in NoSQL can be advantageous in some situations. One situation in particular was when the franchisee system in our company was increasing in size exponentially. We needed the ability to scale up quickly so we could take advantage of all the sales and operational data now available to us. Scaling out, as opposed to scaling up with bigger servers, is the way to go when dealing with increases in data processing loads and this is more easily accomplished through NoSQL databases. Scaling out can be more affordable and can be accomplished more efficiently than scaling up. NoSQL databases also have the ability to handle larger volumes of data, which is important when you need to react quickly to significant changes in data loads in the future. There are certainly more developers familiar with relational databases versus NoSQL databases, but the extra training is well worth the benefits in my opinion. In addition, although relational databases may have more connectivity to analytics tools, I believe the future is bright for NoSQL databases in this regard as more analytics tools are being developed."

There are advantages and disadvantages of using one type of database over another. In your answer, try to communicate what you know about each and through your example show how you would apply your knowledge to an actual project.

Do you have experience introducing new data analytics applications? If so, what obstacles did you run into while introducing and implementing them?

"In my last position as a Data Engineer, I took part in the roll-out of a new data analytics application in my company. Introducing new applications takes careful planning to attempt a smooth transition to the new tool. With my experience, I found that even with careful planning, unforeseen circumstances can occur. In the early stages of the tool's introduction, we had unexpected high demand for user licenses. This required reallocation of resources to purchase additional licenses as well as reprioritization of training schedules to meet the needs of different departments. We also had to ensure that our infrastructure could support the significantly higher number of employees utilizing the tool."

Introducing new data analytics applications within a company does not happen frequently, because it's cost prohibitive. However, when companies do roll out new data analytics tools, it can become quite a production as the tool has to be connected to the company's current systems, training has to be administered to relevant users, and regular maintenance of the tool needs to be managed. If you have never had experience introducing a new data analytics application, be sure to explain what you know about the process of doing so. This will let the interviewer know that, when the situation arises, you will have some foundational knowledge to help you through the process. If your experience ran smoothly, point out what possible scenarios may have presented themselves as obstacles and how you may have handled them.

Do you have extensive experience working in a cloud computing environment? What benefits and challenges do you see working in one?

"The prior companies I have worked for did not utilize a cloud computing environment. However, I am aware that many people feel that working in this type of environment may compromise data security and privacy since data is not kept within the walls of the company. In addition, you have limited control as the infrastructure is controlled by the service provider. However, benefits likely would include cost savings and more reliability as downtimes would be minimal since most service providers grant agreements guaranteeing a high level of service availability. In my opinion, whether cloud computing is right for a specific company would highly depend on the structure of its IT department and the resources available to it."

It is well known in the industry that there are benefits and challenges to cloud computing. Whether or not you have experience working in a cloud computing environment, it is important to convey your understanding of the benefits and challenges. It reflects your understanding of current issues and technology in the industry. In addition, if the company you are applying to does utilize a cloud computing environment, at the very least, they will be assured that you are aware of possible issues that may arise from it.

Do you have experience building data systems using the Hadoop framework? If so, explain a particular project in detail.

"Yes, I have had experience utilizing the Hadoop framework on a project where my team was tasked with increasing data processing efficiency. The Hadoop framework was implemented to increase data processing speeds without compromising quality through its distributed processing. Hadoop was also chosen for its scalability as our organization anticipated our data processing needs would increase significantly in the near future. This project had limited resources so Hadoop was an attractive choice because it has a open-source framework. It is also Java-based which reduced the training required as most of our team was familiar with the language."

Hadoop is one of many tools a hiring manager may specifically inquire about. Being asked about your experience with a specific tool or language is most likely an indication that it is something used on the job. Prepare for the interview by researching which languages and tools they use at the hiring organization. In most cases, this would be outlined in the job description. If you have little or no experience with the specified tools or languages, conduct research to at least show you have some knowledge of them. If you do have experience with the tool, make sure the explanation of your project reflects your knowledge of its capabilities.

Data Engineers work closely with Data Architects. How do you see your job differing from that of a Data Architect?

"In my experience, the difference between the two roles have varied depending on the organization. People in the two roles work closely together, but I believe have some general differences in their responsibilities. A Data Architect builds the data architecture for the organization's data systems and manages the servers. He/she maintains a holistic view of how the data moves into and throughout the organization. Data Engineers also may have a role in the building of the architecture, but in my experience his/her role is concentrated more on the testing and maintenance of it. In addition, the Data Engineer ensures high quality and reliable data is available to analysts in other parts of the organization."

In asking this question, the interviewer is likely trying to gauge your understanding of different roles within a data warehouse team (or the department in the company responsible for maintaining the databases). There is no 'right' answer to this question as the roles and responsibilities of Data Engineers and Data Architects differ from one company to another and may also have some overlap.

Which ETL tools do you have experience utilizing? Do you prefer one over the others? If so, why?

"Through my college coursework, I have been exposed to a variety of ETL tools and have experience with some of them including SAP Data Services, IBM Infosphere and SAS Data Management. Based on this experience and various industry articles I have read, I found Informatica's PowerCenter to be the most effective ETL tool because it provides a high level of flexibility when it comes to changes in the business and has also been known for its high performance rate. Flexibility and high performance rates are important attributes of an ETL tool as it guarantees that, despite changes in the business or its structure, its data operations will continue to run smoothly with the data remaining accessible."

In the process of answering this question, the interviewer should be reassured that you are familiar and have experience with the ETL process. Therefore, after you list the ETL tools with which you have experience and declare your preferred one, be sure your reasons for your preference reflect your knowledge of the ETL process.

"I have experience using both PowerCenter and Oracle Warehouse Builder, but based on my experiences, prefer PowerCenter. I have found that, relative to other ETL tools, the advantages to using PowerCenter include: a single point of control which strengthened security, universal connectivity that allowed for data access and integration from several different sources and scalability that kept us ready for future increases in data needs."

"While working with several different ETL tools in prior jobs, I have learned that there are usually pro's and con's to each. I have experience using Microsoft SSIS, Oracle Data Integrator and Informatica PowerCenter. If I had to select one ETL tool over another, based on my experience, I would select Oracle Data Integrator. My preference is based on its higher level of performance from using a EL-T architecture. This allows the transformation stage to occur after data has been loaded on the target server thereby reducing network traffic and ultimately cost. I also find its ability to detect data issues automatically before data is loaded to be an important feature as it prevents data quality issues and maintains a higher level of efficiency in the process."

Which programming/scripting languages do you have experience utilizing? Which one do you have the MOST experience with?

"The languages I have had the most exposure to are SQL, Java, R and Python. I am most comfortable using SQL since this is the language I have used most often in various school class projects. However, I enjoy expanding my programming knowledge base by learning new languages. I realize that sometimes one programming language may not be the best solution in every scenario."

In most cases, job descriptions will outline which programming language skills are preferred or required for the job, so going into the interview you should have this information if it was made available to you. Specify which language you have the most experience with, but circle back to the preferred/required programming language(s) if you have used it/them in past projects so you at least let the interviewer know you have had some exposure to it.If you do not have experience with it/those, the hiring manager must have found other valuable aspects of your resume that would be beneficial to the job. If this is the case for you, concentrate your answer on the language you have the most experience with and highlight any similarities it may have to the required or preferred language. In addition, highlight any strengths you may have with learning new concepts or languages. You want to convey to the interviewer that you will be dedicated to working with the systems and programs that the company has in place, even if it requires additional training.

"I have experience programming with both Python and R, but most of my work has been done using Python. I understand the job summary specified that R is preferred, so I want to assure you that I firmly believe I can strengthen my R programming skills quickly while working on the job. I tend to pick up concepts quickly and learn the best while performing tasks."

"Through my past data-related work, I have gained experience using Python, Java, R and SQL. If I had to pick the language I have the most experience using, it would have to be SQL. In each of my prior positions, I have had the invaluable opportunity to learn a new language or gain more exposure to one that I have less experience with. In these situations, I have learned quickly and been able to use these languages in varying capacities."

Working with data can present a variety of challenges. Can you think of a time where you ran into an unexpected challenge bringing together data from different sources? How did you end up overcoming this challenge?

"While working in my previous positions, my teams and I have always strived to anticipate data issues that may occur during the ETL process. However, inevitably, an unforeseen issue or circumstance will arise. In particular, I worked for a company that included a franchise system that required data to be collected from a variety of locations and systems. One of the franchisees changed his system without notice which created loading issues for his store's data. I worked to develop a short-term solution to get the crucial data into our system for corporate-wide reporting and also began developing a long-term solution to avoid similar situations in the future. "

Your answer to this question should showcase your problem-solving skills and how you react to unexpected challenges. The interviewer may ask for a situation related to your previous experience working with data, as specified in the question listed, or may leave it as more of a general question about dealing with challenges. If you have not experienced the specific situation, think of how you would approach the challenge if you encountered it in the future.

Describe a project you worked on where you played an active role in solving a business problem through the innovative use of existing data.

"While working for one of my previous employers, I was engaged in an initiative aimed at identifying the cause of the company's high employee turnover rate. By examining data from other areas of the company including Marketing, Finance and Operations, I identified key areas that required further examination. I found the data in these areas of the company had some high correlations with employee turnover rates. I worked with the analysts in these departments to better understand these correlations. This led to strategic changes within these departments that had a positive effect on the employee turnover rate."

'Innovative' and 'active' are the key words in this interview question. Hiring managers would like to see that you are vocal and actively contribute your ideas and experience to a project. If possible think of an example where you may have initiated or took charge of a project. It will be important to emphasize your role in coming up with the innovative solution to the problem and to not spend too much time explaining the problem itself.

Data Engineer Interview Questions

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1. Describe a time when you found a new use for existing data that had a positive impact on the business.
2. What do you find to be the most difficult aspect of being a Data Engineer?
3. What led you to a career in Data Engineering?
4. If you had the time and resources, what types of training courses would you enroll in to help you in your job as a Data Engineer?
5. Do you have experience training others on thing such as software, applications, porcesses or architecture? If so, what have you found to be the most difficult aspect of doing so?
6. Some in the Big Data industry consider Data Engineering to be a non-analytic career path. Do you agree or disagree with that statement and why?
7. Outside of your technical skills, what skills do you find most valuable as a Data Engineer? These could be skills you attained from jobs unrelated to data.
8. Do you prefer work in more of a Generalist role or concentrate your work on the Pipeline or Database?
9. Are you comfortable working 'behind the scenes'? Alternatively, are you comfortable coming out and being in the 'spotlight'?
10. Which certifications have you earned that are applicable to your job as a Data Engineer? Which was your most recent one and when did you earn it?
11. What kind of experience have you had working with Data Scientists and what skills do you have in common with them?
12. Do you have experience building custom analytics applications? If so, please describe the application you created.
13. Have you ever been in a data disaster recovery situation? If so, describe the situation and your role.
14. Give an example of when you proposed changes to improve data reliability and quality. Did these changes end up being implemented? If not, why not?
15. Data maintenance is one of the many responsibilities of a Data Engineer. In many cases, tasks related to this are fairly routine. Describe a time when you encountered a data maintenance problem that required you to deviate from your "routine".
16. Do you have experience with data modeling? If so, what data modeling tools do you have experience using?
17. What is your experience level with NoSQL databases? Give me an example of a project/situation where you found building a NoSQL database to be more appropriate than a relational database.
18. Do you have experience introducing new data analytics applications? If so, what obstacles did you run into while introducing and implementing them?
19. Do you have extensive experience working in a cloud computing environment? What benefits and challenges do you see working in one?
20. Do you have experience building data systems using the Hadoop framework? If so, explain a particular project in detail.
21. Data Engineers work closely with Data Architects. How do you see your job differing from that of a Data Architect?
22. Which ETL tools do you have experience utilizing? Do you prefer one over the others? If so, why?
23. Which programming/scripting languages do you have experience utilizing? Which one do you have the MOST experience with?
24. Working with data can present a variety of challenges. Can you think of a time where you ran into an unexpected challenge bringing together data from different sources? How did you end up overcoming this challenge?
25. Describe a project you worked on where you played an active role in solving a business problem through the innovative use of existing data.

Data Engineer Interview Questions

1. Describe a time when you found a new use for existing data that had a positive impact on the business.

2. What do you find to be the most difficult aspect of being a Data Engineer?

3. What led you to a career in Data Engineering?

4. If you had the time and resources, what types of training courses would you enroll in to help you in your job as a Data Engineer?

5. Do you have experience training others on thing such as software, applications, porcesses or architecture? If so, what have you found to be the most difficult aspect of doing so?

6. Some in the Big Data industry consider Data Engineering to be a non-analytic career path. Do you agree or disagree with that statement and why?

7. Outside of your technical skills, what skills do you find most valuable as a Data Engineer? These could be skills you attained from jobs unrelated to data.

8. Do you prefer work in more of a Generalist role or concentrate your work on the Pipeline or Database?

9. Are you comfortable working 'behind the scenes'? Alternatively, are you comfortable coming out and being in the 'spotlight'?

10. Which certifications have you earned that are applicable to your job as a Data Engineer? Which was your most recent one and when did you earn it?

11. What kind of experience have you had working with Data Scientists and what skills do you have in common with them?

12. Do you have experience building custom analytics applications? If so, please describe the application you created.

13. Have you ever been in a data disaster recovery situation? If so, describe the situation and your role.

14. Give an example of when you proposed changes to improve data reliability and quality. Did these changes end up being implemented? If not, why not?

15. Data maintenance is one of the many responsibilities of a Data Engineer. In many cases, tasks related to this are fairly routine. Describe a time when you encountered a data maintenance problem that required you to deviate from your "routine".

16. Do you have experience with data modeling? If so, what data modeling tools do you have experience using?

17. What is your experience level with NoSQL databases? Give me an example of a project/situation where you found building a NoSQL database to be more appropriate than a relational database.

18. Do you have experience introducing new data analytics applications? If so, what obstacles did you run into while introducing and implementing them?

19. Do you have extensive experience working in a cloud computing environment? What benefits and challenges do you see working in one?

20. Do you have experience building data systems using the Hadoop framework? If so, explain a particular project in detail.

21. Data Engineers work closely with Data Architects. How do you see your job differing from that of a Data Architect?

22. Which ETL tools do you have experience utilizing? Do you prefer one over the others? If so, why?

23. Which programming/scripting languages do you have experience utilizing? Which one do you have the MOST experience with?

24. Working with data can present a variety of challenges. Can you think of a time where you ran into an unexpected challenge bringing together data from different sources? How did you end up overcoming this challenge?

25. Describe a project you worked on where you played an active role in solving a business problem through the innovative use of existing data.