The 21st century can be called the “Data Era,” as everything revolves around the four-letter word DATA. Today, no company can function effectively without access to data. No one else is responsible for collecting or managing it. You’d be correct in assuming that they enjoy code and data. There are no codes involved; instead, this task goes well beyond codes.
Here’s where someone with strong technical experience in data pipelining and performance optimization, such as a Data Engineer, may make a significant contribution.
It is the fastest rising tech career through 2022. Proper abilities and certifications through the best data engineering bootcamp can create a career in tech. Let’s know more about data engineering and its prospects.
What is data engineering?
Designing and creating systems for storing, analyzing, and collecting large amounts of data is known as “data engineering.” It’s a wide-ranging field that can be applied to various industries. For organizations to collect vast amounts of data, they need the necessary people and technology to ensure that the data is ready for analysis when it reaches the data scientists and analysts.
By 2025, we’ll produce 463 exabytes of data per day. Data engineers can make a real difference by helping data scientists make their lives easier. How many bytes of data is that? One and a half to 18 zeros. Without data engineers to analyze and channel that data, fields like machine learning and deep learning would fail.
In what ways does a data engineer contribute to a company’s success?
Analytical data is prepared by a Data Engineer who works with the data. These are the people in charge of designing, building, and maintaining the large-scale processing system’s architecture in great detail. However, each organization’s role is unique. Building a data pipeline to gather all the information from many sources would be a typical task for a data engineer. Finally, the data is organized and summarized for better analysis.
Why is data engineering one of the most rapidly expanding professions in technology?
Information gathering, storage, and analysis are becoming increasingly important in today’s technological environment. Even if there are some similarities, the tasks performed by data scientists and data engineers are somewhat different. An engineer is responsible for designing and maintaining technological systems that allow data analysis by a data scientist.
In particular, the need for data engineering is on the rise. Increasingly, organizations demand a mix of capabilities (data science and data engineering) or a lot more of the latter. Two years ago, companies were scrambling to find data science talent. Those same firms have now understood the importance of employing data scientists and engineers with a breadth of experience in both the fields of data science and machine learning. A company needs ten data engineers for every three data scientists it employs.
It’s also worth noting that their typical salary is higher than that of data scientists in most circumstances. Data engineers earn 20-30% more than data scientists in many businesses. Soon, data engineers will make more money than any other profession, and their pay is increasing rapidly as a result. In addition to the fact that most organizations are moving to the cloud, the demand for data engineers has risen due to enterprises’ growing attention to data preparation.
Big data engineering services are increasing in demand:
Big data engineering services supplied by consulting firms and other tech organizations are a final sign that demand for Data Engineers is growing fast. Big data and data engineering services are clearly in high demand worldwide. From 2017 to 2025, growth predictions range from 18% to a staggering 31% p.a. Data transformation projects appear to be a long-term investment, with no end in sight. As a result, there will be a high need for Data Engineers shortly.
Data engineers are in limited supply:
Here are a few variables that contribute to the scarcity.
Training in a specialized area
Data Scientists can retrain in economics, biostatistics, physics, and mathematics to become Data Scientists. For Data Engineers, reskilling on the job from, for example, a background in Finance isn’t an option.
To become a Data Engineer, one must possess a wide range of skills. “Fake it till you make it” isn’t an option in this industry. First, you need a wide range of abilities. Certification or an intensive self-study program is often required to enter this career.
To make matters worse, the field of Data Engineering is undergoing rapid transformation. As part of LinkedIn’s 2020 Emerging Jobs study, it identified several “unique” talents that Data Engineers should possess. There was Spark, Hadoop, and ETL on this list.
Finding skilled Data Engineers is highly challenging due to the wide range of highly specialized and constantly growing skillsets required.
How is data engineering distinct from other data roles?
Data engineering is distinct from data analyst and data scientist roles. Most people misunderstand these data roles. Let’s get rid of them!
The data analyst’s job is to take all of the numerical and non-numerical data, analyze it, and then translate it into something that the general public can understand. In other words, the primary duties of a data analyst involve collecting, analyzing, and transmitting data. Upper management relies on it to make well-informed judgments on its direction.
Data engineer salary
Data engineering also pays handsomely. According to Glassdoor, the average compensation for data engineers in the US is $111,933. Data engineers are well compensated compared to comparable data occupations like data analyst ($68,000) or database administrator ($81,444).
Data engineer career path
Not all data engineers are entry-level positions. Many data engineers begin their careers as software developers or business intelligence analysts when starting. Progressing up the corporate ladder might lead to responsibilities such as director of product management, director of information technology, or chief data scientist.
To summarize, data engineering is a job where you never work on the same item for years, and mastering it takes a lifetime, enabling data engineers to upskill for future job profiles. Without a doubt, this job requires a high level of big data competence. For this, corporations pay data engineers well.