Big Data Engineer Interview Questions

Big Data Engineer Interview Questions

Big Data Engineer Interview Questions

Everything you need to know about hiring a Big Data Engineer

Why hire a Big Data Engineer?

IDC predicts that the global data volume will reach 175 zettabytes in 2025. Now, that’s enormous data literally. But the data will make sense only when it is processed and analyzed through frameworks. This is where a Big Data Engineer comes into the spotlight.

According to Thomas H.Davenport, Visiting Professor at Saïd Business School, University of Oxford, 

“Every company has big data in its future, and every company will eventually be in the data business.” 

As a fast-evolving career, Big Data Engineers are ubiquitous in every data-driven industry from agriculture to climate change.  So, if you are a data wizard and have flair for numbers and coding, then this job is made for you. 

On the other hand, hiring Big Data Engineers is a roller coaster ride for many hiring managers as you need to look beyond technical skills and expertise. The job role differs based on project types. As your best friend, our tech hiring guide is here to the rescue.

Learn the essentials of hiring a Big Data Engineer in a fuss-free way with our tech hiring guide.

What is a Big Data Engineer? 

A Big Data Engineer is an IT professional who designs, builds, tests, and maintains complex data processing systems that work in tandem with large data volumes.

On any given day, you can see a Big Data Engineer performing the roles of software developers, coding, data scientists, statisticians, and engineers simultaneously.

In short, the job revolves around 6 Vs: 

  • Variety
  • Volume
  •  Velocity
  • Variety
  • Veracity
  • Value

Are Big Data Engineers and Data Scientists similar? No. But there is an overlap between the roles. 

Big Data Engineers build and maintain the systems and processes for data collection and extraction while Data Scientists analyze the cleaned data through various predictive models for meaningful insights.

Why are Big Data Engineers in high demand?

Do you know by 2025,  the world will produce 463 exabytes (or more)per day? Big data has transformed the way businesses used to operate. But, the data is least productive unless a data engineer processes and channels it.

According to Wikibon,

Global Big Data market revenues for software and services are forecasted to reach $103 bn by 2027, with a  CAGR of 10.48%.

 In fact, an Accenture study strongly advocates companies embrace big data to avoid losing their competitive position or worse, face extinction.  

Conjunctively, Forbes adds that,

Jobs in Machine Learning Engineering, Data Science and Big Data Engineering are among the top emerging jobs on LinkedIn.

The U.S.Bureau of Labor Statistics predicts the growth of the Big Data Engineer job market at 22%  between 2020 and 2030. 

Recommended Read: The Growth of Candidate Assessment Technology

Average pay for Big Data Engineer

As per Glassdoor estimates, the national average for a Big Data Engineer salary in the USA is $1,04,463 per year, with an added compensation between $2,342 – $30,427.

Big Data Engineer KPIs

A Big Data Engineer’s role is highly rewarding as they are responsible for translating data into revenue in their business organization.

Here are the KPIs to measure a Big Data Engineer’s work potential.

  1. Data quality
  2. Incident severity and resolution time 
  3. Data uptime and frequency
  4. Development velocity
  5. Testable user stories for data measurement

Big Data Engineer Job Description

As a professional responsible for the Big Data infrastructure, a Big Data Engineer’s job is as vast as the data itself. From developing data pipelines to performance optimization, their role is no smooth ride.

Take a look at the Big Engineer’s  job description in the following template:

  1. Leverage Data Engineering expertise to multiple teams across our organization
  2. Establish fault-tolerant, adaptive, and accurate data computational pipelines
  3. Steer data profiling activities of source system candidates
  4. Lead efforts to document source-to-target mappings
  5. Direct efforts to design and implement logical data models focusing on usability 
  6. Configure and ensure conformed dimension design and modeling across the RDS 
  7. Responsible for Performance and ease of use of data and information
  8. Liaison with other designers and architects to build ergonomic data models
  9. Write programs in Python for data cleaning and processing activities
  10. Execute project through an agile management process

Big Data Engineer Interview Questions

1) Can you name the 6 big Vs of Big Data?

2) How does big data help increase business revenue? Tell me an example

3) How do you deploy a Big data solution? What steps do you follow?

4) Differentiate between HDFS and YARN

5) What role does Hadoop play in Big data?

6)  What is important- good models or good data? Why do you think so?

7) How do you execute a Hadoop cluster?

8) Name some important features of Hadoop

9) How does NFS differ from HDFS?

10) Can you explain the NameNode recovery process?

Recommended Read: Stop Asking these Boring Interview Questions

Best Practices for hiring Big Data Engineers

Hiring a Big Data Engineer means making better business decisions.  Most of the job platforms say that careers in Big Data Engineering are on the rise since 2020.

In this regard, hiring big data engineers can help business enterprises to accelerate data as revenue models.

Many video interviews suggest that recruiters go all out to find quality talent as a Big Data Engineer is a rare combination of analytical and business skills. Therefore, hiring them is a top priority for the data team. 

On the flip side,  a majority of the recruiters are still stuck in the age-old recruitment methods. That’s why hiring red flags like over-reliance on credentials rather than competencies and lack of advanced evaluation tools in skill assessments have severely limited the talent pool.

Recruitment software like Glider AI takes candidate evaluation to the next level. Through a structured and standardized process, interviews are made candidate-friendly and also accurately assess skills and competencies. Hiring is not only bias-free but evaluated on real-world scenarios as well.

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