Data Engineering Career Track

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by Sharon Wilfong

Published September 1, 2022

Reviewed by Monali Mirel Chuatico

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Edited by Kelly Thomas
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Data is the world's most valuable resource. Fortune Business Insights' technology and media market research report indicates the global big data analytics market was worth $240.56 billion in 2021.

The report also projects the big data market will grow from $271.83 billion-$655.53 billion between 2022 and 2029. Organizations use big data analytics to understand market trends, improve operations, increase sales, and provide a better customer experience.

Big data comes from multiple sources, such as customer databases, social networks, emails, and medical records. It can also be contained in network logins, sensors, devices, GPS, or manufacturing equipment. It can also be machine generated.

Additionally, big data often incorporates external consumer data, geographic information, images, videos, and audio files. Big data can be complicated to collect and store and needs processing to analyze information correctly.

It requires the right tools, complex algorithms, and experts to take advantage of the tremendous amount of available data.

What Is Data Engineering?

Industries from finance, healthcare, and communications to education and government organizations need data engineering to decipher ever-growing stockpiles of information. Data engineering enables efficient organization and processing of big data so it's usable.

But what exactly is data engineering? First, when data sets are scattered, answering specific questions is more complicated.

Consequently, data engineering encompasses designing and building the systems and processes to collect raw data from multiple sources. Once the system collects the data into one location, it processes the information into unified data sets.

Data engineering provides analysts with a clear and concise way to examine the information for customer service, human resources, and marketing. Businesses and organizations can utilize big data engineering to make better-informed decisions across all departments.

What Do Data Engineers Do?

Data engineers use programming languages like Python, Java, and SQL and other digital tools such as Matillion, Snowflake, and Apache Airflow to design processing systems for big data.

These professionals work from the design phase to completion. They collaborate with data architects, software engineers, and product/project managers to plan project goals and scope.

Data engineers develop data models, ETL and pipeline designs, and data platforms. They ensure that data systems meet specific requirements and often oversee disaster recovery plans.

Additionally, they conduct research to improve data quality, efficiency, and reliability. These highly skilled professionals must be committed to continuous education to keep up with advancing technologies.

Data Engineering Career Outlook

The Bureau of Labor Statistics (BLS) projects 13% growth in computer and information technology jobs between 2020 and 2030. A 33% projected growth in jobs for information security analysts during that period, according to the BLS, suggests a bright outlook for tech professionals.

However, the BLS doesn't collect information specifically on data engineers. Still, businesses and organizations of all sizes benefit from data engineering, providing opportunities in the field.

In August 2022, Indeed determined that the average data engineer salary in the United States is $136,280 annually, based on 4,900 salaries reported. ATT, Amazon, Wells Fargo, and HP are some big names that were looking for data engineers on the job search site.

How to Become a Data Engineer

A data engineer must be well-versed in programming languages, database architectures, and predictive modeling and commit to continuous education. Here's how to become a data engineer.

  • Education: Beginners can start with free online courses or invest in a coding bootcamp to build their skills. Data engineers typically hold a bachelor's degree, and many have an advanced degree, such as a master's in data science.
  • Gain Work Experience: Data engineering takes problem-solving, analytical skills, and the ability to navigate complex challenges to find efficient solutions. Work experience as an analyst, database administrator, or architect can help professionals develop the skills and mindset to succeed.
  • Certifications: The Cloudera certified professional data engineer designation certifies professionals in data analysis, ingestions, staging, storage, transformation, and workflow development. The Google Cloud certified professional data engineer designation certifies proficiency in building data structures, designing systems, and analyzing for security. Snowflake also offers advanced SnowPro certification for data engineers.

Frequently Asked Questions About Data Engineering Careers

Is data engineering a good career?

A variety of industries and organizations hire data engineers. So these professionals may find opportunities and room to grow during their careers. A data engineer's salary may reach over $100,000 per year.

Indeed determined the average data engineer salary for professionals with over 10 years of experience was $175,620 in the U.S., as of August 2022. These professionals are typically part of a data team in large corporations such as Wells Fargo or ATT.

However, it typically takes education and experience to reach the skill level required to succeed at the job. Zippia analyzed 2,742 data engineer resumes and found that 65% have a bachelor's degree, while 22% have a master's degree.

Is data engineering a growing field?

Data might be the world's most valuable resource, but it's challenging to use without proper processing. LinkedIn's 2020 Emerging Jobs Report indicates the need for data engineers has increased by almost 35% since 2015.

As industries from all sectors gather massive amounts of data, the need for these skilled professionals will likely continue to grow. LinkedIn's report notes some of the top industries hiring data engineers include financial services, hospitals, healthcare, and information and technology.

Additionally, the technologies data engineers use to gather and process big data are ever-expanding. For example, LinkedIn's report shows that since 2015, Amazon Web Services has emerged as a top skill for data engineers.

Is data engineering stressful?

A survey of 600 data engineers from October 2021 illustrates that data engineering can be stressful. The survey shows that burnout plagues 97% of data engineers, and 70% plan to leave their job within the year.

The survey concludes that the relative newness of the role means its complexities aren't yet understood. For example, 91% of data engineers reported receiving requests with unrealistic expectations, and 61% said this happens often.

Maintaining data quality is essential for data engineers. However, 50% of the professionals surveyed said spending too much time finding and fixing errors limits their time working on more impactful projects.

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