Blog Tech Job Descriptions Why the Data Engineering Career Path is Thriving

Why the Data Engineering Career Path is Thriving

For #BacktoSkills month, we’re spotlighting a series of skills that supercharge careers. Last week we looked at Python, and this week we’re talking about the skill that’s driving the ‘data driven’ revolution: Data Engineering.

If you’re interested in a career that’s high paid, in demand in most industries, and enabling some of the biggest advancements in tech, you’d be hard pressed to find many better options than Data Engineering. Over the last five years, job openings for Data Engineers have grown by 30%, which is significantly higher than average job growth in the US. Plus, Data Engineers make over $110,000 a year according to Glassdoor

While the job growth and salary are incredibly appealing, it’s a good idea to know what to expect from a career before diving in. Here’s what you need to know about this role to get started on your Data Engineering career path. 

What is a Data Engineer?

Data Engineers gather, clean, and organize different types of data from various sources like websites, apps, databases, and more. They make sure the data is accurate, complete, and easy for other people to access and use. Data Engineers also make sure that systems are built for scale; data is constantly flowing in from different sources, and these engineers make sure that volume can be handled appropriately, without impacting data retention or integrity. 

It might be helpful to distinguish Data Engineers from Data Analysts or Data Scientists.

Data Analysts typically work with the data sets provided by Data Engineers to solve tangible business problems, while Data Scientists can be viewed as more advanced Data Analysts who use more advanced data techniques – like their own machine learning algorithms or predictive modeling processes – to generate more complex predictive insights. 

Data Engineers can be viewed as the essential bridge between the source data and the Analysts, Scientists, Machine Learning experts, and others who consume and manipulate that data.

Future of Data Engineering (& AI)

It’s appealing to break into the Data Engineer career pathway because big data analytics is projected to grow significantly over the next few years.

According to Dice Media’s analysis, the global big data analytics market is projected to have a booming growth rate of 30.7%, ultimately being worth $346.24 billion by 2030. In fact, over the past 12 months, we’ve seen tremendous growth already, with the market value of data related skills increasing by 2% on average, and data engineering, data strategy, and Big Data Analytics achieving pay premiums of 18-20%. 

Many are worried that AI could replace a Data Engineer’s role or completely disrupt the practice, but there is nothing to fear. Some standard tasks could be automated by AI – such as data cleaning and transformation or to generate synthetic data for testing- but AI will require a lot of professionals who have advanced and complex data skills and who can develop, fine-tune, and manage AI-powered systems. At least in the near future, businesses looking to leverage AI will likely look to hire more Data Engineers, not less.

What Skill You’d Need

Data engineers are engineers at heart, which means they should have strong programming skills and a solid understanding of distributed systems.

To excel as a data engineer, it’s important to know how to build complex database queries with SQL and NoSQL, and have experience with big data software (like Apache Airflow, Hadoop). You’ll also need to know how to program in at least one scripting language, like Python or Scala. 

Additionally, having experience in security and scalability will help a data engineer stand out as a candidate.

Career Possibilities For a Data Engineer

The typical data engineering career path is not unlike the path for other kinds of software engineers. Depending on prior engineering experience, a new data engineer might work as a regular software engineer, a data engineering intern or possibly even a data analyst

From there, the path is straightforward, going from entry-level data engineer, to senior-level data engineer, to lead data engineer, and even executive roles like head of data engineering or chief data officer.

The roles available will depend on the size of the organization, as smaller companies might have their data department fall under an engineering umbrella.

Your Path to Data Engineering

It’s never been easier to become a Data Engineer.x Udacity’s catalog has courses tailored to different experience levels, starting from absolute beginners to seasoned developers looking to enhance their skills. 

For those just starting down this path, you’ll want to build a strong foundation in Python and SQL; Udacity’s Programming for Data Science Nanodegree program is a great first step.

Individuals who already have that foundation but are newer to Data Engineering should look to our Data Engineering Nanodegree programs, which have the option to learn with AWS or Microsoft Azure.

If you’re already a Data Engineering practitioner and want to take your skills to the next level, check out our Data Streaming or Data Architect Nanodegree programs. Or, browse our full suite of courses that teach Data Engineering and follow your curiosities!

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