If you are considering a career in big data and look forward to holding a data engineering role, in that case, you are on the right trajectory. Or, if you are an expert looking for a new job opportunity, preparing for an upcoming interview can be overwhelming. The good report is that the industry is experiencing rapid growth with many job opportunities and career options.
Considering the competitive nature of the data engineering industry, preparing for your interview session is in your best interest. Below are some top data engineering questions you are likely to expect in the interview. We also have a sample of responses to help you perform and impress your recruiter.
1. What Do You Understand By The Term Data Engineering?
Big data has altered how we conduct business, leading to a demand for data engineering experts. These professionals collect and manage large quantities of data. In my view, data engineering involves designing and building systems for accumulating, storing, and examining large amounts of data.
It facilitates brands to collect massive amounts of data and convert it into usable information. Data engineering allows experts to build systems for collecting, managing, and transforming raw data into relevant and applicable information. Experts like data scientists interpret this information allowing organizations to evaluate and optimize their performance.
2. What Interests You Most About Data Engineering
My choice to study data engineering stems from an interest in technology and my desire to continue my Information System degree. My passion for data engineering has encouraged me to transition and work in a similar field. Having started from an entry-level position as a business intelligence analyst, I must say that I’ve gained immense experience allowing me to qualify for more prominent roles. What I enjoy most is the opportunity to learn and gradually increase my understanding and positively impact this industry.
3. What Qualities Do You Possess To Help You Succeed In This Role?
From experience, I believe a data engineer needs technical skills to distribute systems or data stores, create reliable systems, and effectively combine data sources. My strength in math, coding, and cloud computing capabilities have played a role in my success in this field. This role effectively requires proficiency in coding languages, including SQL, Python, NoSQL, R, Java, and Scala.
I also have adequate knowledge of the working of relational and non-relational databases as they rank as the most common data storage solutions. Another helpful skill to have is in Extract Transform and Load. ETL is moving data from databanks to another single source, such as a data warehouse. Some ETL tools I am proficient in handling include Talend, Xplenty, Stitch, and Alooma.
Having data storage familiarity helps to identify the type of data storage type to use. Whenever you design a data solution, you must determine when to use a data lake and when to use a data warehouse. Such information is essential for any data engineer to navigate and ensure a smooth operation in handling complex tasks.
Though machine learning is a preserve of data scientists, it is very beneficial for a data engineer to grasp basic concepts to understand the job’s requirements better. Cloud computing and storage are becoming increasingly popular as people trade physical options for online cloud services.
Though most companies have a dedicated data security unit, some data engineering roles include securely managing and storing data to protect it from damage or theft. Additionally, this role needs problem-solving, communication, and leadership skills if one is to grow and thrive in it.
4. Are You Familiar With Any Critical Frameworks Or Applications In Effectively Managing This Role?
In my five-year working experience, I have had the opportunity to perfect my data engineering skills. These skills include a solid foundation in programming, statistics, and big data technological skills. Some of the common technical skills I’ve learned include:
- Apache Hadoop and Spark,
- Amazon web services
- Amazon S3
- Hadoop Distributed File System (HDFS).
I look forward to developing my knowledge in Tableau, PostgreSQL, MongoDB, Apache Kafka, and Hive. I believe that to thrive in data engineering requires familiarity with popular data science programs.
5. State The Difference Between A Data Analyst And A Data Engineer
Data engineers build systems for assembling, validating, and organizing high-quality data. Data engineers gather and prepare data. Likewise, data scientists or data analysts, on the other hand, analyze information to extract insights that promote better business decisions.
6. Why Did You Take A Career In Data Engineering?
While a career in engineering is rewarding in many dimensions, I love the challenges that come with it. This has helped me develop my creativity in developing practical solutions. A data engineering career is critical to an organization’s success as it provides easier access to data to facilitate every industry stakeholder in task delivery.
Data engineering is very marketable in today’s business space as there’s a huge demand for engineers everywhere. It is a top trending career path in the tech industry, beating web development, computer science, and database management.
7. What Options Are Available For Growth In Your Career Path?
Soon after graduating from high school, I knew that data engineering was the career path I desired to take. The versatility in the job option attracted me to this industry. Data engineering doesn’t necessarily have to start from an entry-level position. You have an opportunity to start as a software engineer and progress to other managerial roles and become a machine learning or data engineer.
8. Why Should We Consider Your Application And Hire You? What Makes You Different From The Rest?
Apart from my academic qualifications and work-related experience, I am a certified expert in data engineering. I got this certification two years ago from the Associate Big Data Engineer. I’ve also undertaken other certification examinations from Google Cloud Certified Professional Data Engineer.
I intend to take another exam hoping to get the IBM Certified Data Engineer certificate. Passing this exam will help me qualify for more extensive opportunities with more responsibilities in the data engineering field. With my vast experience, I believe I have what it takes to impact your organization positively.
9. Please Share With Us Your Experience And Proficiency In Handling Data Engineering Tasks.
Soon after graduating from college with a degree in Software Engineering, I got an entry-level position as a database administrator in an international manufacturing firm. I worked in that role for two years, getting a lot of exposure. I learned new skills from here that have helped me qualify for more significant projects.
I was fortunate to get a scholarship to advance in Data Science. After graduation, I handled different roles in software engineering, data science, and information systems units. I believe that my vast experience can highly benefit your company because my qualifications meet the requirements per the job description.
10. As An Expert In Data Engineering, What Strategies Would You Undertake When Developing A New Product?
The first would be to get an overview of the entire project to help me understand the complete scope and determine the project’s requirements. I would then take time to understand the stakeholders’ expectations. Later, I would brainstorm and create multiple possibilities to use for the pilot stage. With my knowledge and experience, I would start developing data tables and continue progressing depending on the initial outcome.
11. What Is The Difference Between Data Engineering And Data Science?
Data science involves extracting data from massive databanks, also known as big data. Data science can operate in various industries, including government, industry, or applied science. The main aim of data science is to extract and analyze data and derive insights that are relevant to their field of study.
Likewise, a data engineer’s job involves incorporating multiple complex system components. It also includes extracting relevant information, which necessitates the creation of complicated data channels. These data channels take raw data from various sources and direct them into a single larger structure for storage.
12. Data Engineering Consists Of Terminologies That Help Define Processes. What Does The Term Namenode Mean?
NameNode is the foundation on which the HDFS system operates. It helps track the source of the data file and stores the source in a single file system.
13. From Your Experience, What Are The Effects Of A Namenode Crash?
HDFS cluster has one NameNode that tracks DataNode metadata. Due to the single nature of the NameNode in an HDFS cluster, it becomes the primary source of a fault. If NameNode crashes, the system becomes inaccessible. Sometimes, a passive NameNode acts as a backup and takes over if the primary one is to fail.
14. What Do You Understand By The Term Hadoop?
Hadoop is an open-source framework. It is proficient in processing massive chunks of diverse data sets in a distributed style across hardware clusters, engaging a simplified programming model. It implements a secure, shared storage and analysis system operated on large data sets distributed across groups of commodity computers. These computers are widely affordable and readily available.
15. What Does Hdfs Stand For?
In Hadoop, data exists in a distributed file system referred to as a Hadoop Distributed File System (HDFS), similar to data in a personal computer’s local file system. Its processing model rests on a concept where computational logic helps cluster servers containing data. Computational logic is a variant of a programming language that processes data stored in the Hadoop Distributed File System.
16. Describe Some Standard Hadoop Features That You Are Familiar With
- Hadoop can efficiently handle any data, including structured and unstructured MySQL data, which is structured, XML, and JSON which are semi-structured, and unstructured videos and images.
- It provides faster data processing.
- Data is copied across multiple DataNodes in a Hadoop cluster, ensuring data availability irrespective of whether one of your systems fails.
- Hadoop is exceptionally scalable as a significant volume of data. It is shared across several devices and processed separately. The number of these devices can either be increased or decreased.
17. How Would You Distinguish Between Structured And Unstructured Data?
- Structured data is less flexible and relies on the schema, while unstructured data is more flexible.
- Structured data is stored in DBMS, while unstructured storage is in unmanaged file structures.
- Structured data is more challenging to scale, unlike unstructured data.
- Structured data can perform a structured query which helps in enhancing its performance, while unstructured data’s performance is low.
18. Which Hadoop Components Are You, Familiar, With?
My experience in data engineering has given me adequate expertise. Some standard Hadoop components include Hadoop YARN, a management resource component that manages cluster resources to prevent overloading a single device.
Hadoop has a processing unit that works on slave nodes in MapReduce Technique that delivers to the master node. Likewise, Hadoop’s storage unit is HDFS which stores data by distribution. It constitutes two parts, a name, and a data node. There exist multiple data nodes but only one name node. Additionally, Hadoop Common refers to a collection of tools and libraries.
19. Name Characteristics Of Big Data
From my knowledge, I believe the four characteristics of big data revolve around volume, velocity, variety, and veracity.
20. What Does Data Modelling Refer To?
Data modeling visualizes a partial or entire information system to identify links between structures and data. Doing this reveals the types of data used that is stored in the system. It also reveals the relationship between them. As well as showing its format, features, and how data is classified and arranged.
In retrospect, data options fit needs and requirements at certain levels of abstraction. The process starts when end-users and stakeholders provide valuable information about business requirements. Conversion of these data structures helps to create a tangible database design.
21. How Many Design Schemas Are Available In Data Modelling?
Data modeling has two primary design schemas available in data modeling. These two are snowflake and star schemas.
22. Distinguish Between A Block And Block Scanner In Hdfs?
In HDFS, a block refers to the least possible amount of data people can interpret. Alternatively, a block scanner helps track the list of blocks on a DataNode and examines them for checksum issues. Block scanner utilizes throttling technique to save disc bandwidth on the data node.
23. From Your Experience In Data Engineering, What’s The Outcome When The Block Scanner Senses A Corrupt Data Block?
Whenever a block scanner detects a corrupt data block, several procedures can help resolve the anomaly. First, whenever this happens, DataNode alerts the NameNode. From there, NameNode starts creating a new duplicate from a corrupted block replica. Then, there’s a comparison of the replication factor from the replication count of the proper representations. If a match occurs, the faulty data block will not be detached.
24. From Your Experience, Which Python Libraries Are Ideal For Processing Data Efficiently?
Python is a popular programming language beneficial in data engineering. To process data effectively, I would consider using NumPy, which helps process numbers, or using Panda which is ideal for statistics – the basis of data science. It is also an excellent option for preparing data used in machine learning.
25. Imagine A Scenario Where You Have An Increase In Data Volume. What Procedure Would You Integrate To Add More Capacity To The Data Processing Structure?
There are several options to consider. I could collect data from IoT devices in the hope of rolling out many more devices, which will send back sensor data to the data pipeline. Data processing will occur in two ways, but storage occurs in three ways. These three ways include a database, a data warehouse for examinations, and caching layer for interaction between a backup system and a control panel web app.
Other possibilities include more database instances in the cloud on Microsoft Azure, Google Cloud, or Amazon Web Services. I have also found removing an old data set, data compression, or redirecting subsets of data to other parts of the system to be very beneficial.
26. What Procedure Would You Use To Validate A Data Migration From One Database To Another?
Validation may occur as data flows in both databases. Or, the validation may occur once a complete data migration happens. To ensure a successful data migration, you must validate the schema as part of the migration.
Also, conducting a cell-by-cell comparison using QuerySurge guarantees full validation of the data. This is because it avoids time-consuming and expensive data quality issues. Automation is very essential in QuerySurge type of testing.
Another helpful option is performing reconciliation checks on the source and targeting databases for all columns. This prevents the data from being corrupted, helps maintain date formats, and fully loads the data.
Other reliable options include integrating a NULL validation, conducting Ad Hoc Testing, or conducting non-functional testing. Following these strategies and testing procedures during a data migration exercise will ensure efficiency during the migration process.
27. Share The Most Significant Challenge You’ve Overcome In The Data Engineering Industry.
In a past role, I worked as a lead data engineer for a project with less staffing. As a result, my project allocation kept lagging behind schedule, causing a lot of inconveniences and a risk of disciplinary action.
After my team missed the first milestone, I took the initiative and approached the project manager to suggest possible adjustments. The reason for the delay was understaffing, forcing us to work longer hours. Based on my suggestions, the organization assigned additional staff to my team. It became easier to manage workflows, and we completed the project successfully within the remaining timeline.
28. Would You Prefer A Pipeline-Centric Or Database Approach When Handling Projects?
Most organizations I’ve partnered with are small and medium-sized enterprises. Because of this, I am a generalist who is flexible and can work efficiently with databases or a pipeline focus. My long-term industry experience gives me a comprehensive understanding of distributed systems and data warehouses.
29. What Are You Bringing To Our Organization?
My wide range of professional experience and academic qualifications make me an ideal candidate for this role. Being in the data engineering industry for over seven years gives me the confidence to deliver and meet your expectations.
Over time, I have led multiple teams and different departments, which have helped me to develop my leadership skills. Working in busy environments has also enhanced my problem-solving capabilities and communication skills. If given a chance, I believe that I can make a positive contribution to your organization.
30. Which Aspect Of Data Engineering Do You Least Enjoy?
While technology is a driving force in the data management industry, staying up to date on industry-related information can become a challenge. Every other day there’s a new and better way of tackling projects. Though keeping up is a struggle for me, I have learned to navigate around it.
Reading is not my favorite hobby. I’ve always struggled keeping up with opening and reading emails, blog posts or newsletters that I receive from sources that I have subscribed to. Because of this, I keep lagging in terms of technical information. But, nowadays, I spend my free time watching industry-related documentaries and news updates to keep up with technology. I prefer watching videos to reading, which has helped me keep in touch with whatever is happening in the data engineering industry.
Almost every company currently relies on big data. This demand keeps propelling the data engineering industry to grow in leaps and bounds. Our businesses are increasingly dependent on expertise to scale and advance. Technology has led to a shift in how companies operate. Whether you are looking for employees in data engineering or are actively searching for employment, we hope that the questions and answers here have shed some light on how to handle an interview.
If you are a recruiter, ensure that each question you ask delivers insights about a data engineer job applicant’s knowledge and experience—structure questions to bring out their proficiency and capabilities. As an interviewer looking to hire an expert, we hope you can identify how to rate and shortlist competent candidates, based on their responses and overall disposition.