Data engineering is one of the fastest-growing tech careers. It offers a competitive salary with room for growth—ample reasons to start training for this career path. The data engineering skills gap is driving demand upward, in a trend that seems likely to continue as more and more businesses switch to a data-driven model.
Considering entering this field? Here’s what you need to know.
What Is Data Engineering?
In the age of big data, companies constantly collect data about customers, sales, and marketing, to name just a few areas. It’s the job of a data scientist to analyze that load of data and make sense of it. Data engineers are tasked with building data pipelines so that data analysts can access the data they need in a usable format.
Data engineering is the whole process of setting up and maintaining a data infrastructure so that data is stored in a safe and centralized location, where it’s available to everyone who might need it.
Typically, large organizations store data in a variety of different formats and databases. Inventory records might be kept in a SQL server, while sales data might be stored in a CRM platform. This kind of data storage siloing makes it difficult for anyone in the firm to get a big-picture understanding of the whole operation. It can also mean that data is hard to access across different divisions of the organization.
A data engineer integrates all the company’s data into one data warehouse, where it can be correctly formatted for use whenever data scientists need to study it.
Why Are Data Engineers in High Demand?
In 2019, Dice named data engineering the fastest-growing tech profession. The field has been growing by 50% year on year and shows no sign of slowing down. In 2021, Interview Query found that interviews for data engineers had increased by 40% over the previous year.
Data engineering jobs are in high demand as more businesses transform to a cloud-based and data-driven model. Not so long ago, the tech field was the only one that really focused on data analytics. Today, just about every sector from agriculture to manufacturing relies on the insights it can extract from data.
As the demand for usable data increases, so does the demand for skilled data engineers. You can expect this demand to continue to soar. Chatbots, cloud-based contact centers, artificial intelligence applications, etc., all rely on accessible data.
How Much Do Data Engineers Earn?
Data engineers are very well compensated. In some parts of the United States, salaries are on the rise. Hired’s 2019 State of Software Engineers report found that the salaries for data engineers had risen by 7% in New York City and by 6% in the San Francisco Bay Area.
In 2020, Glassdoor found that data engineers’ salaries ranged from $110,000 to $155,000. The average salary was $137,776, and the median was $102,472. The Robert Half salary guide listed similar figures, although it put the midpoint of the salary range for big data engineers a bit higher at $163,250.
5 Reasons to Choose a Data Engineering Career in 2021
There are numerous benefits to pursuing a data engineering career, ranging from personal to practical. Growing demand and substantial compensation are among the numerous reasons why this path can be a great choice for the right candidate.
1. Data engineering is foundational
Machine learning, data science, artificial intelligence, and all other data applications are built on data engineering. Data engineers are charged with feeding data into the analytical applications that are created by data scientists. Without that reliable, steady stream of data, such applications would not be achievable.
In part, this means that data engineering is a good place to start building a career in data science. It also means it’s an essential role that’s unlikely to fall out of favor.
2. Data engineers are in demand
Working in an in-demand field gives you enormous leverage and freedom. Want to work remotely from Hawaii, or would you rather work in a booming tech company in San Francisco? When you’re part of an in-demand profession, a lot of choices open up to you. As of May 27, 2021, Glassdoor listed about 114K data engineering job openings in the United States.
3. Data engineers are highly paid
We’ve mentioned salary, but it bears repeating. Data engineers can expect to draw a salary ranging from $100,000 to $163,000.
As an entry-level data engineer with less than a year of experience, you can expect to earn a little over $77,000. The pay is location-specific, so engineers working out of New York City or San Francisco will earn the highest salaries ($153,000 or $159,000 per year, respectively).
4. It’s a challenging field
People who are talented in computer science will stagnate if they’re stuck in a low-effort, repetitive job. It’s been said that engineers, in general, love to be challenged and need the stimulation of new problems to solve on a constant basis.
Data engineering provides that challenge. Whether it’s coping with a constant flood of data, maintaining overstrained data pipelines, or navigating the shift to the cloud, data engineers are constantly thinking on their feet to solve problems, which is a draw for certain personality types.
5. Data engineering skills are transferable
Data engineering is closely linked to careers like data analytics, data science, artificial intelligence, and other related career tracks. If you started out as a data engineer, you have the option to shift to a different but related field if you’re willing to go through an extra training period.
Data engineers can also transition to work in software engineering after learning a web framework like Django (Python), Express (Node), or Spring Boot (Java). While the knowledge sets are different, the skills are transferable.
Ready to switch careers to data engineering?
Data engineering is currently one of tech’s fastest-growing sectors. Data engineers enjoy high job satisfaction, varied creative challenges, and a chance to work with ever-evolving technologies. Springboard now offers a comprehensive data engineering bootcamp.
You’ll work with a one-on-one mentor to learn key aspects of data engineering, including designing, building, and maintaining scalable data pipelines, working with the ETL framework, and learning key data engineering tools like MapReduce, Apache Hadoop, and Spark. You’ll also complete two capstone projects focused on real-world data engineering problems that you can showcase in job interviews.
Check out Springboard’s Data Engineering Career Track to see if you qualify.