Engineers specialize as the necessity arises. Due to increased digitalization, there is a surplus of data nowadays. Because of this, the demand for data engineering and related data infrastructure is growing at an accelerating rate. As a result, workers and equipment are needed to complete the task.

In the context of data science and artificial intelligence, data engineering is a subset of data science. Since roughly 2016, the demand for Data Engineers has exceeded the supply. According to a new report, there has been a 50% year-over-year increase in data engineers’ number of open opportunities. However, within and between teams that produce and consume data, the fast expansion over the previous 15 to 20 years has created some interesting interactions.

Are you unsure if a job in Big data engineering is right for you or not? Big data engineering is becoming a highly sought-after professional path because of the availability of courses in Spark and Hadoop. To begin a career in big data engineering, you should take the big data engineering certification course.

Spend some time reading this article to become familiar with the reasons to pursue a career in big data engineering.

Big Data Engineering

As businesses and organizations generate vast amounts of data, this data must be stored scientifically to provide valuable inputs as needed. Big data engineering abilities allow companies to quickly gather and process enormous amounts of data, allowing them to make better decisions and expand faster as a result of this.

2015-2020: The Data Engineer’s Rise

The abundance of data necessitated a new type of analytics: self-serve analytics. The idea was appealing—let any team acquire any data they wanted.

But it wasn’t that easy. Data engineers might aggregate data into a central warehouse and add analytics capabilities. Collecting all of the data proved to be a difficult task. Centralized self-serve analytics remained an issue due to the proliferation of tools and datasets across teams.

The advancements in non-IT software and data tooling practically changed the industry. But these gains triggered an integration dilemma. Individual teams loved having point solutions. But those tools’ incapacity to communicate was a concern. Data engineering emerged as corporations grappled with these issues (or re-born). The quest to combine and use all this data began.

Expectedly, the data tooling industry responded, resulting in enormous technological and adoption advances throughout this period. In 2020, demand for data engineers surged by 45%. The 2021 Data Science Interview Report indicates a 40% increase in data engineering interviews in the last year. Also, according to the exact estimate, demand for data science employment (which includes data engineering) would rise 38% in the next decade. It suggests you will have work in the following years. You can only expect more labor.

Reasons behind demand in Data engineer job growth

1. Big data engineering services growth

Global big data as well as data engineering services are in high demand. From 2017 to 2025, growth is expected to range from 18% to 31% annually. Companies continue to spend on data transformation efforts. Big data engineering services from consulting firms like Accenture and other IT businesses like Cognizant are significant indicators of the need for Data Engineers. It indicates high future demand for Data Engineers.

2. Universality

Nearly every organization will soon use data in a significant way. Data-driven management and innovation will become more widely accepted across all sectors and industries for the first time. Soon, even small and medium-sized businesses will use data-driven solutions and make decisions based on that data. There will be additional work for data engineers as a result of this.

3. Organization

One quintillion data sets are created every day by the entire world’s population. Most of it is still disorganized. Soon, data documentation and categorization are going to be a top priority. To make their efforts more efficient, companies will endeavor to arrange and catalog all of the information they have at their disposal. Data that hasn’t been documented, cleaned, or tested in any way is swiftly outnumbering the data that has been utilized, analyzed, and comprehended. Unfortunately, this is the case today. That’s a significant issue that has to be addressed.

4. Acceleration

We need to develop new algorithms and ways to move and analyze data more quickly as the volume of data grows at an astronomical rate. That’s the final piece of the puzzle that will form the future of data engineering. New methods of interpreting data will have to be developed in the future, and current systems will need to be improved.

5. Automation

You can automate many tedious and repetitive operations in the data engineering field. There will be more advanced intelligence technologies available to data engineers in the future. Consequently, they will complete their tasks faster and with less effort. Because of this change, achieve more in less time and focus just on the most critical tasks. Smart data management solutions will handle everything routine and simple.

6. Specialization

Data-related employment will likely become increasingly fragmented and decentralized as this trend continues. As a result, businesses will employ a broader range of experts to work on increasingly complicated and vast data projects. There is also the possibility that data engineers could soon be divided into backend and frontend teams.

7. A variety of possibilities

Big data can transform the way people work and live, and it has the power to bring possibilities across the board. You can apply big data engineering in various sectors, including environmental protection, toxic emission analyses, and weather pattern research. Analytical professionals will always be in high demand, and businesses will increasingly turn on Big data engineers in the years to come.

As a business intelligence analyst or database administrator, many data engineers begin their careers in entry-level positions. As your career progresses, you’ll be able to take on more responsibility and develop your career. Suppose you’re starting in your career or changing directions. In that case, the Data Engineering professional credentials can help you gain the job-ready skills you need.