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15 Big Data Engineering Interview Questions to Ace Your Interview

Raw data is like clay in the hands of data professionals such as data engineers. These individuals specialize in data engineering, the discipline of designing and developing systems and structures around data that enable people to access it from various sources and in multiple formats for the purpose of collection, storage, and analysis. 

Using these systems and structures, people can explore the real-world applications of the data that in turn, allow companies to make important decisions for advancing their business plans. Big data engineering, as the name suggests, is when the volumes of data being engineered is on a massive scale and the data itself is complex.

India’s data engineering market is predicted to grow from USD 18.2 billion in 2022 to USD 86.9 billion in 2027. With trillions of bytes of data being generated, data engineers play a significant role in making it usable for data scientists, analysts, and decision-makers. 

Unsurprisingly, data engineers are in demand and can command a median annual salary of INR 17.0 lakhs. With over 30,000 open jobs, employers are on the hunt for the right talent and lining up interviews on priority. 

The data engineer’s role is definitely a competitive profession and a lot depends on how you, as an aspiring candidate can ace your big data engineering interview questions. These interviews vary in intensity as questions are structured typically based on the experience levels and fall into the categories of: 

• Fresher or beginner
• Intermediate
• Advanced

Let’s say you’ve been called in a for an interview. What are the odds that you will eventually land the role? The answer is in being prepared. It will dramatically improve the odds in your favor. This means doing your homework, research, and study ahead of the interview. 

It could be your first shot at being a data engineer but knowing the kind of interview questions and answers for freshers to expect, will give you a strong head start. 

Preparing for your Data Engineeing interview

The most important thing is to not be overwhelmed. Especially if you are new to this career path, begin by congratulating yourself on having landed an interview. The next logical steps would be to know your potential employer, look up company reviews on Glassdoor etc., brush up on the skills required, and also research on the questions you might be asked.

While the questions will be mostly technical, you should expect some generic ones in the set of interview questions and answers for freshers. These might be on the lines of an intro to who you are, how you define a data engineer’s role, and what prompted you to consider this choice of profession. 

Keep yourself mentally prepared with confident answers in your own words rather than parroting something you read online – that will help build a good first impression and keep them interested. 

With regards to the technical aspects of the interview, revising your skills and keeping details of your past experiences and professional understanding at your fingertips is a good idea. 

And yes, practicing your answers aloud or getting a friend to take a mock interview will help build your confidence and iron out any creases in your preparation. 

You could plan your revision as part of this self-review based on the following:

• SQL is the core requirement. It’s the most commonly understood language between a data engineer and data consumers and recommended because of its accessibility and portability. 

Supplement it with a solid understanding of Hadoop technologies such as algorithms for Machine Learning, databases, messaging platforms, web notebooks etc. as well as data pipeline systems that are used to solve big data problems. 

• Based on the job profile, you would need to either learn or revise your knowledge of specific languages. For example, the primary requirement would be Python if you’re going to be working on data science or analytics projects. Or you might need to know Scala for backend-oriented systems.
• Demonstrate your practical understanding of the job at hand and also back it with your solid theoretical understanding of the tools and technologies involved. One of the best ways to do this is that you share the prototype of a data pipeline you have designed.

Prepare to answer any questions the interview have regarding the problem statement, vision, end goals, and various criteria that you have factored into your design such as ingestion sources, output destinations, duplication, loading data, testing and validation, type of scaling etc. 

• If you have some years of experience, one way to highlight your practical knowledge and core capabilities is to identify some of your best projects and prepare how you would talk through the major highlights around each.

Ace your Big Data Engineering interview questions with confidence

As we mentioned earlier, the common interview questions faced by big data engineers can be categorized into beginner, intermediate, and advanced. Regardless of the category you belong to, keeping yourself familiar with usual questions is important.

 It’s equally important to be prepared with well-thought-out responses that are not just straight from an online reference or technology manual but also reflect your actual understanding of the field. 

• Name the foundational elements of Big Data?

Also called the four Vs, the following comprise the foundational elements of Big Data

The following forms to be the vital foundation of Big Data:

• Volume
• Variety
• Velocity
• Veracity

Pro tip: Explain why each of the above are considered vital elements.

• Define the discipline of Data Engineering in your own words.

A: One way is to define Data Engineering as a specialized discipline used to design and create systems that allow data scientists, analysts, and business strategists to gather and evaluate raw data from various sources and in multiple formats. These structures enable data professionals and consumers to convert raw data into useful information, discover real-world applications of the data, and enable strategic decisions to aid business. Several industry-leading data engineering tools are now being used in collection and storage of big data for various purposes.

Pro tip: while many definitions are available, explain it in your own terms with examples and analogies of real-life applications to convey your practical understanding.

• Briefly describe the differences between Data analysis, Data science and Data engineering 

Data Analysis is the process wherein numerical data is studied and interpreted to help businesses make informed decisions.

Data Science is all about analyzing and interpreting complex data, which is wrangled and structured into big data. 

Data Engineering, is distinct from the above two disciplines as it is more concerned with designing and building storage systems for collecting, storing, and analyzing data at multiple scales.

• Briefly explain the basic principle of Data Modeling and the distinctive design schemas used.

A: Data Modelling is the process through which fetched data is processed and transformed into relevant data before it is shared with appropriate people who will be consuming it. The main objective of Data Modeling is to simplify complex software designs using visual representations of data objects mapped to associated rules that define them. As a result, even the most complex software designs will become easy to comprehend.

The design schemas used in data modeling are 

Star schema: 

Snowflake schema:

Pro tip: If prompted by the interviewer, you should be prepared to describe the two different schemas in detail.

• Why is Hadoop considered the gold standard in Big Data engineering?

As an open-source framework, Hadoop is popularly used to store and manipulate data. In addition, applications run on clusters through the Hadoop framework. It summarizes data, analyses data, and performs data queries.

The framework makes available massive space where data can be stored and as a twin advantage, it enables powerful processing capabilities where an infinite number of jobs and tasks can be done in tandem.

Pro tip: If prompted by the interviewer, you should be prepared to describe the two different schemas in detail.

Interview questions and answers for intermediate level

• How does Big Data Analytics improve business productivity for a company?

Quickly explain the role of data in today’s increasingly digital world – how it’s being generated and consumed in every sphere of life. Talk about how Data Analytics is helping digitally mature organizations harness their data and use it to identify business revenue streams, strategize growth, streamline operations, improve productivity etc. Specifically, give examples such as how it’s used to predict customer behavior, personalize marketing campaigns, and improve engagement and retention opportunities.

Pro tip: While these are generic examples, using references from your own sphere of experience can be more impressive and demonstrate your practical understanding.

• How would you differentiate between a Data Warehouse and an Operational Database?

The key differentiator is how either of them can be used in data analysis. Data Warehouse is a better fit because it simplifies the analytical process as it focuses on  aggregations, calculations, and select statements. 

In contrast, operational databases using Delete SQL statements, Insert, and Update are more about speed and efficiency, which may not make it easy for data analysis to take place. 

• What has been your prior experience in Data Modeling?

Explain the years of experience you have had in Data modeling and mention any projects that you have been involved in. You could mention various industry tools that are popularly used such as Informatica etc. 

Pro tip: If so, say it. If not, simply being aware of the relevant industry tools and what they do would be helpful.

• Summarize the role of Hadoop in providing data security.

Briefly talk about the growing importance of Hadoop security and then explain the various stages of securing data in the Hadoop ecosystem:

• Authentication
• Authorization
• Auditing
• Data protection using encryption, data masking etc.
  

• Briefly mention the different modes in which Hadoop can be used.

Talk about the three main usage modes for Hadoop listed below:

• Standalone mode
• Pseudo distributed mode
• Fully distributed mode

Pro tip: Be prepared to give a brief description of the modes and the different situations in which they are applicable.

Interview questions and answers for advanced level

• Have you worked in our industry/sector before?

Your response will help the interviewer understand if you’ve had relevant industry experience that can supplement your technical skills in big data engineering. If you have the experience, explain with example projects, tools etc. that will give them a clear idea of the range of your knowledge. 

• Talk us through a challenging project that you worked on and what made it memorable for you?

This will not only test your technical competence and practical experience but also any complementary skills and strengths or weaknesses that you have while dealing with a tough work situation. 

Pro tip: Structure your response to paint a clear picture of the problem statement for the project or work situation. Then explain your specific contributions that helped resolve the challenges such as initiative-taking research, leadership qualities, collaboration etc. will help build your case for senior roles. Be open about any setbacks and lessons learnt that you overcome as well. 

• Talk about the various components available in the Hive data model.

The Hive data model uses components referred to as:

• Buckets
• Tables
• Partitions

• Mention the complex types of data collections in Hive.

Complex datatypes may be classified in terms of:

• Array
• Map
• Struct
• Union

• Pro tip: Be prepared to give a brief description of the different situations in which each type is applicable.

• How are skewed tables used in Hive?
  Explain how skewed tables contain values that are repetitive and occur in large numbers compared to other data in the table. Skewed data is isolated and stored separately. 

Must-have skills for a data engineer

• Coding: you are expected to know SQL, NoSQL, Python, Java, R, and Scala.
• Cloud storage and data warehousing – knowledge of AWS (Amazon web services) and Google cloud. 
• Knowledge of OS – Windows OS, Mac OS, Linux operating systems and also the components of infrastructure and the architecture of your OS. 
• Database Systems – SQL databases, NoSQL databases, relational databases, and cloud databases and how to store big data on storage servers.
• Use tools such as Apache Spark, Power BI, Tableau. 
• Critical Thinking
• Understanding the basics of Machine Learning
• Persuasive communication skills

Being forearmed will help you be well-armed ahead of the interview and ace your performance, which can be achieved through the right certifications. Enrol into Hero Vired’s Certificate Program in Data Engineering to acquire the required skills and techniques of data engineering and contribute to business revenues by enabling smarter decisions.