Data engineers are vital members of any enterprise data analytics team, responsible for managing, optimizing, overseeing, and monitoring data retrieval, storage, and distribution throughout the organization.
Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines used by data scientists, data-centric applications, and other data consumers.
This IT role requires a significant set of technical skills, including deep knowledge of SQL database design and multiple programming languages. Data engineers also need communication skills to work across departments and to understand what business leaders want to gain from the company’s large datasets.
Data engineers are often responsible for building algorithms for accessing raw data, but to do this, they need to understand a company’s or client’s objectives, as aligning data strategies with business goals is important, especially when large and complex datasets and databases are involved.
Data engineers must also know how to optimize data retrieval and how to develop dashboards, reports, and other visualizations for stakeholders. Depending on the organization, data engineers may also be responsible for communicating data trends. Larger organizations often have multiple data analysts or scientists to help understand data, whereas smaller companies might rely on a data engineer to work in both roles.
According to Dataquest, there are three main roles that data engineers can fall into. These include:
Data engineers are responsible for managing and organizing data, while also keeping an eye out for trends or inconsistencies that will impact business goals. It’s a highly technical position, requiring experience and skills in areas such as programming, mathematics, and computer science. But data engineers also need soft skills to communicate data trends to others in the organization and to help the business make use of the data it collects. Some of the most common responsibilities for a data engineer include:
Data engineers and data scientists often work closely together but serve very different functions. Data engineers are responsible for developing, testing, and maintaining data pipelines and data architectures. Data scientists use data science to discover insights from massive amounts of structured and unstructured data to shape or meet specific business needs and goals.
The data engineer and data architect roles are closely related and frequently confused. Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles. They visualize and design an organization’s enterprise data management framework. Data engineers work with the data architect to create that vision, building and maintaining the data systems specified by the data architect’s data framework.
According to Glassdoor, the average salary for a data engineer is $117,671 per year, with a reported salary range of $87,000 to $174,000 depending on skills, experience, and location. Senior data engineers earn an average salary of $134,244 per year, while lead data engineers earn an average salary of $139,907 per year.
Here’s what some of the top tech companies pay their data engineers, on average, according to Glassdoor:
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The skills on your resume might impact your salary negotiations — in some cases by more than 15%. According to data from PayScale, the following data engineering skills are associated with a significant boost in reported salaries:
Only a few certifications specific to data engineering are available, though there are plenty of data science and big data certifications to pick from if you want to expand beyond data engineering skills.
Still, to prove your merit as a data engineer, any one of these certifications will look great on your resume:
For more on these and other related certifications, see “Top 8 data engineer and data architect certifications.”
Data engineers typically have a background in computer science, engineering, applied mathematics, or any other related IT field. Because the role requires heavy technical knowledge, aspiring data engineers might find that a bootcamp or certification alone won’t cut it against the competition. Most data engineering jobs require at least a relevant bachelor’s degree in a related discipline, according to PayScale.
You’ll need experience with multiple programming languages, including Python and Java, and knowledge of SQL database design. If you already have a background in IT or a related discipline such as mathematics or analytics, a bootcamp or certification can help tailor your resume to data engineering positions. For example, if you’ve worked in IT but haven’t held a specific data job, you could enroll in a data science bootcamp or get a data engineering certification to prove you have the skills on top of your other IT knowledge.
If you don’t have a background in tech or IT, you might need to enroll in an in-depth program to demonstrate your proficiency in the field or invest in an undergraduate program. If you have an undergraduate degree, but it’s not in a relevant field, you can always look into master’s programs in data analytics and data engineering.
Ultimately, it will depend on your situation and the types of jobs you have your eye on. Take time to browse job openings to see what companies are looking for, and that will give you a better idea of how your background can fit into that role.