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.
What is a data engineer?
Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers. Their primary responsibility is to make data available, accessible, and secure to stakeholders.
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. They’re often responsible for building algorithms for accessing raw data, too, 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, they 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.
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The data engineer role
According to Dataquest, there are three main roles that data engineers can fall into. These include:
Generalist: Data engineers who typically work for small teams or small companies wear many hats as one of the few “data-focused” people in the company. These generalists are often responsible for every step of the data process, from managing data to analyzing it. Dataquest says this is a good role for anyone looking to transition from data science to data engineering, as smaller businesses often don’t need to engineer for scale.
Pipeline-centric: Often found in midsize companies, pipeline-centric data engineers work alongside data scientists to help make use of the data they collect. Pipeline-centric data engineers need “in-depth knowledge of distributed systems and computer science,” according to Dataquest.
Database-centric: In larger organizations, where managing the flow of data is a full-time job, data engineers focus on analytics databases. Database-centric data engineers work with data warehouses across multiple databases and are responsible for developing table schemas.
Data engineer job description
Data engineers aren’t only responsible for building tools to access raw data, but also managing and organizing that data while keeping an eye out for trends or inconsistencies that could 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:
Develop, construct, test, and maintain architectures
Data acquisition
Develop data set processes
Identify ways to improve data reliability, efficiency, and quality
Prepare data for predictive and prescriptive modeling
Data engineer vs. data scientist
Data engineers and data scientists often work closely together but serve very different functions. While data engineers develop, test, and maintain data pipelines and data architectures, data scientists tease out insights from massive amounts of structured and unstructured data to shape or meet specific business needs and goals.
Data engineer vs. data architect
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, on the other hand, work with the data architect to create that vision, building and maintaining the data systems specified by the data architect’s data framework.
Data engineer salary
According to Glassdoor, the average salary for a data engineer is $115,487 per year, with a reported salary range of $77,000 to $176,000 depending on skills, experience, and location. Senior data engineers earn an average salary of $170,466 per year, while lead data engineers earn an average salary of $173,185 per year.
Here’s what some of the top tech companies pay their data engineers, on average, according to Glassdoor:
CompanyAverage Annual SalaryGoogle$214,807Meta$212,869Amazon$194,467Apple$188,313Cisco Systems$177,586IBM$130,826
Data engineer skills
Coursera suggests learning the fundamentals of cloud computing, coding skills, and database design to start a career in data engineering. Common programming languages used in data engineering include SQL, NoSQL, Python, Java, R, and Scala. Familiarity with relational and non-relational databases is a big plus, as is understanding extract, transform, and load ETL systems. Common ETK tools include Xplenty, Stitch, Alooma, and Talend.
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:
JavaScript: +25%
MapReduce: +24%
Oracle: +23%
Perl: +20%
Amazon Redshift: +19%
Apache Cassandra: +15%
Django: +14%
Project Management: +12%
Natural Language Processing (NLP): +10%
Apache Sqoop: +10%
Data engineer certifications
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.
Google Professional Data Engineer
For more on these and other related certifications, see Top 8 data engineer and data architect certifications.
Becoming a data engineer
Many data engineers start as software engineers or business intelligence analysts before transitioning into data engineering. 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. A bachelor’s degree in computer science is common.
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’ll 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.
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