Is Data Engineering Dying? 4 Reasons Why It Isn’t


The hype around data engineering has more and more jumping the ship and working as data engineers. At the same time, some people believe that data engineering will soon go out of fashion and be dead. So is it a safe career, or will you have to switch in the next decade or so because it’s no longer ‘the thing’?

Data engineering is not dying. Though the responsibilities and skill requirements of the position will change over time as we make technological advancements. Data engineers will need to keep up with the changes and grow their skill set to stay relevant. But data engineering is here to stay.

In this article, we’ll discuss four big reasons why data engineering will not die anytime soon. We’ll also look at how you can keep growing as a data engineer to ensure you don’t lose your job in the future.

Important Sidenote: We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

4 Reasons Data Engineering Is Not Dying

With every hype, there’s a peak, and then there’s the downfall. You may have already read articles about data science and data engineering ‘dying out’. What these blog posts and videos are basically saying is that things won’t remain the same. But then again, they never have. Data science and machine learning professionals will need to keep growing and learning as new tools, techniques, and concepts are introduced.

However, as far as the question of companies stopping to use data to grow their business is concerned, it will never happen. This means data science—and by extension, data engineering—will always stay relevant. Here are four big reasons why data engineering is not dying:

Organizations Are Moving Toward Data-Driven Businesses

Since data science is growing increasingly popular, many big and small companies are hiring data scientists, data analysts, and data engineers to make sense of their data. Organizations now realize that data science is an essential factor in business growth. The businesses that don’t get the most out of their data will soon be outdated.

Automated techniques are required to collect data, analyze it, and gather insights to help grow businesses. For this reason, the demand for data engineers is reaching new heights, and it’ll continue to be a crucial aspect of every company out there.

There Are Tremendous Job Opportunities

Data engineering, data science, and data analytics are some of the fastest-growing jobs in tech. According to StackOverflow’s Developer Survey 2020, data engineers have “a disproportionately high salary compared to developers within a similar level of experience in different roles.”

LinkedIn’s 2020 Emerging Jobs Report also reveals that data engineering is one of the top 10 jobs experiencing massive growth in the United States.

In fact, Dice reports that there will be a shortage of data engineers because the demand is simply too high. So it’s clear that data engineering is skyrocketing. It will continue to grow in this fashion as we become more and more dependent on technology.

Data Engineering Is the Backbone of Data Science

The first and foremost step of a data strategy is to collect and handle structured and unstructured data. Data engineers are the people responsible for building data pipelines that tackle the large amounts of information that enters the company’s systems.

When the data comes pouring in from various sources, the first people to touch it are data engineers. The more efficient data engineers are at cleaning, filtering, and processing that information, the more effective every other step of the data process will be. As long as data science remains a thing (meaning forever), data engineering will also remain relevant.

The World Will Always Need People To Collect and Process Data

We’ve reached a point where we rely heavily on the internet and other technology. We’re generating 2.5 quintillion bytes of data every day, which is too much to process manually. There’s a dire need to automate the collection and processing of the data generated.

This is where data engineers come into the picture. They are the people who code to filter, clean, and organize large amounts of data. In today’s time, data engineers have become almost a necessity.

How To Keep Growing as a Data Engineer?

Data engineering is not dying, but it is certainly changing rapidly. It’s essential to keep up with the changes in the data science field and keep track of how data engineering is evolving. Those who don’t do that will lose their jobs or get stuck in the same ones.

In this section, we’ll look at the critical skills required for data engineering in 2021. The following is what you must know to become an efficient data engineer. New tools and techniques will keep coming, and you’ll need to keep yourself updated and enhance your skills to stay in the game.

Technical Skills

As a data engineer, you require several technical skills to be valuable to the organization. First and foremost, you need to have a strong foundation in software engineering. Apart from that, you need to know how to manipulate database management systems. SQL is the standard language for building databases, while NoSQL databases come in a variety of types.

Programming languages are another crucial part of a data engineer’s skill set. Python has several must-know libraries, and R is also widely used in data science and visualization. To better understand data scientists’ needs, data engineers also benefit from basic knowledge of machine learning. It enables them to construct more effective data pipelines and get models into production.

That’s not all, though. A decent data engineer should know some other things as well, including ETL (Extract, Transfer, Load) tools, Data APIs, knowledge of distributed systems, data warehousing solutions, and a basic understanding of algorithms and data structures. And the five essential programs that every data engineer uses are Apache Hadoop or Apache Spark, Amazon Web Services, Azure, Amazon S3, and C++.

Non-Technical or Soft Skills

Apart from the technical stuff, data engineers also need a few essential soft skills since they have to work with a team. On a typical day, they may interact with machine learning engineers, data scientists, data analysts, and developers. Strong communication skills are crucial for data engineers to ensure efficient collaboration.

Data engineers also need to understand the requirements of their team in order to provide the desired results. A healthy give-and-take relationship needs to be formed so that the projects keep running smoothly. It involves understanding the team members’ requirements, updating them frequently, and resolving their pain points.

Some data engineers only filter, clean, and organize large amounts of data. However, depending on the company, they may also be required to perform data analysis and show their findings to stakeholders.

Excellent public speaking and presentation skills are needed to explain technical concepts and how they help solve a particular business problem. The more effectively they communicate their findings, the more likely decision-makers are to act upon them.

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.

Conclusion

Data science is at the heart of many industries, from healthcare to education to e-commerce. Some people believe that the hype around data engineering will be over in the coming years, and data engineering will die out. However, it is simply not true.

Data engineering is essentially the backbone of every data science project. Data engineers handle the influx of data and organize it so that data scientists and machine learning engineers can use it to solve business problems.

In short, this job is here to stay. However, data engineers must work hard to keep up-to-date with the latest developments.

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed 100+ data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

  1. 2020 Emerging Jobs Report. (n.d.). LinkedIn. https://business.linkedin.com/content/dam/me/business/en-us/talent-solutions/emerging-jobs-report/Emerging_Jobs_Report_U.S._FINAL.pdf
  2. 2020: The year of the citizen data engineer. (2020, June 29). Dice Insights. https://insights.dice.com/2020/01/10/citizen-data-engineer-year-2020/
  3. Becoming a data-driven CEO. (n.d.). BI Leverage, At Cloud Scale, In Record Time | Domo. https://www.domo.com/solution/data-never-sleeps-6
  4. Stack overflow developer survey 2020. (n.d.). Stack Overflow. https://insights.stackoverflow.com/survey/2020#work-salary-by-developer-type-united-states
  5. What skills do you need to become a data engineer? (2020, July 8). Springboard: Online Courses to Future Proof Your Career. https://www.springboard.com/library/data-engineering/skills/#8-essential-data-engineer-technical-skills
  6. How to become a big data engineer: Business data analytics careers. (n.d.). Maryville Online. https://online.maryville.edu/online-masters-degrees/business-data-analytics/careers/big-data-engineer/
  7. How to become a data engineer. (2020, September 26). Ohio University. https://onlinemasters.ohio.edu/blog/how-to-become-a-data-engineer/
  8. What is an analytics engineer? (2021, February 9). Northeastern University Graduate Programs. https://www.northeastern.edu/graduate/blog/what-is-an-analytics-engineer/
  9. What is the difference between a data scientist and data engineer? (2020, July 27). UC Riverside. https://engineeringonline.ucr.edu/blog/what-is-the-difference-between-a-data-scientist-and-data-engineer/

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Daisy

Daisy is the founder of DataScienceNerd.com. Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

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