The emergence of the data engineer role is linked to the explosion of data over the last decade, which has been largely influenced by the growth of social media and new technologies such as the Internet of Things. Experts predict global data production to keep growing, with an overall increase of a factor of 10 expected in the period 2013-2020—that is an enormous growth of 44 zettabytes (44 trillion gigabytes!).
For organizations and companies of all sizes, gathering growing amounts of information presents a huge challenge—how to make sense of vast volumes of data gathered from disparate sources. Even small businesses can benefit from big data, so this is not a challenge unique to larger organizations.
The data explosion creates a growing demand for talented individuals who can understand large quantities of data, in particular, how to prepare data for use with the BI tools that drive smarter business decisions. This is where the data engineer becomes an extremely valuable employee.
Do you understand data? Are you interested in helping organizations and enterprises make sense of the vast amount of data that they collect? If so, read on to find out about data engineering and why you should consider becoming one.
When you finish reading, you’ll understand precisely what a data engineer is, the necessary steps to become a data engineer, and what kind of salary this exciting role demands in a world that needs people to help make sense of raw data and turn that information into actionable insights.
What Is A Data Engineer?
Data engineers develop, construct, test, and maintain highly scalable data management architectures, helping to provide the platforms necessary for businesses to ingest large volumes of data, process that data, and get it ready for use with analytics applications.
The demand for data engineers continues to grow at an extraordinary pace—this report conducted by Stitch showed that 6,500 people on the entire LinkedIn website call themselves data engineers. However, at the time the research was carried out, there were 6,600 listings for data engineering jobs in the city of San Francisco alone!
There is clearly a shortfall of data engineers to meet the growing needs of tech companies in particular, who need people that can build and maintain data systems.
Stitch Report Key Findings: Image Source
How To Become A Data Engineer
As the Stitch report above showed, data engineers often have a background in some form of computer science—42 percent of data engineers in that report graduated from a software engineering role.
A computer science or IT-related degree alone is not enough—companies often require vendor-specific certifications to provide the requisite background knowledge for a data engineering career.
Some examples of specific certifications for data engineering include:
- IBM Certified Data Engineer – Big Data — considered the gold standard of data engineering certifications by many companies, this program provides a background on the Big Data applications of data engineering.
- Google’s Certified Professional – Data Engineer— this certification provides an education in data engineering principles, ensuring individuals have the knowledge to work as professionals in the field.
As you can see, becoming a data engineer requires a hybrid educational approach that combines a university degree in Computer Sciences or IT with certifications from accredited bodies in data engineering, such as Google or IBM.
Data Analyst vs. Data Scientist vs. Data Engineer
Data analysts, data scientists, and data engineers are three related but altogether separate roles. The three often collaborate, but it’s important to note the main differences between them.
- The job of a data analyst is to take data, such as sales figures or market research analyses and turn that data into meaningful information that can help a company or organization make better decisions.
- In other words, data analysts can help businesses become data-driven.
- In the U.S. data analysts can command a median annual salary of $56,164
- Skills that can improve a data analyst’s salary include SAS, R, Python, knowledge of BI tools, and data modeling.
- Data scientists have the in-depth statistical background, business acumen, and machine learning knowledge required to make sense of raw unstructured data. The data scientist must be able to eloquently communicate the results of their findings through data visualization techniques and simple English.
- While data analysts are given guidance in what to look for when analyzing data, data scientists are expected to formulate the questions that can help a business gain insight from data and find answers to those questions.
- Data scientists must have advanced programming knowledge and understand data modeling, while data analysts work on simpler structured SQL or similar databases or with other BI tools/packages.
- Data scientists command a median salary of $91,630. Skills such as Apache Hadoop, Python, machine learning, and big data analytics can increase a data engineer’s salary.
- As we’ve seen, a data engineer is responsible for building and maintaining the software infrastructure needed for managing “Big Data”.
- In other words, a data engineer builds and optimizes the systems that data analysts and data scientists need to perform their jobs. They do this by building data pipelines and using tools and techniques to handle data at scale.
- Data engineers earn a median annual salary of $90,932 per year.
- Skills that improve data engineer salaries include knowledge of Scala, Apache Spark, Java, and familiarity with data warehousing.
Learn More on Data Engineering
There are several online resources that can teach you about data engineering, including:
- Google Cloud platform provides several online courses to learn about concepts relevant to data engineering.
- Udemy is also a good resource, with online courses including one on machine learning for data engineers.
- Coursera also has a course on learning data engineering skills and tools.
The explosion of data is expected to continue over the coming years, creating a huge demand for professionals that can help businesses understand large quantities of data.
Data engineers provide the necessary platform for getting insights from data in businesses and organizations. They achieve this by building and maintaining the required software infrastructure for managing and analyzing large volumes of data.
Becoming a data engineer typically involves a hybrid education approach with a background degree in computer science or IT, and certifications from accredited bodies such as IBM and Google.
The high salary commanded by data engineers and exceptional job prospects make this a rewarding and exciting career that is definitely worth considering if you are interested in the subject matter.