What is a data engineer? An analytics role in high demand
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 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.

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 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:

  • Develop, construct, test, and maintain architectures
  • Align architecture with business requirements
  • Data acquisition
  • Develop data set processes
  • Use programming language and tools
  • Identify ways to improve data reliability, efficiency, and quality
  • Conduct research for industry and business questions
  • Use large data sets to address business issues
  • Deploy sophisticated analytics programs, machine learning, and statistical methods
  • Prepare data for predictive and prescriptive modeling
  • Find hidden patterns using data
  • Use data to discover tasks that can be automated
  • Deliver updates to stakeholders based on analytics

Data engineer vs. data scientist

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.

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 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 $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:

CompanyAverage annual salary
Amazon$130,787
Apple$168,046
Capital One$124,905
Hewlett-Packard$94,142
Meta$166,886
IBM$100,936
Target$183,819

Data engineer skills

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:

  • Ruby: +32%
  • Oracle: +26%
  • MapReduce: +26%
  • JavaScript: +24%
  • Amazon Redshift: +21%
  • Apache Cassandra: +18%
  • Apache Sqoop: +12%
  • Data Quality: +11%
  • Apache HBase: +10%
  • Statistical Analysis: +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.

Still, to prove your merit as a data engineer, any one of these certifications will look great on your resume:

  • Amazon Web Services (AWS) Certified Data Analytics – Specialty
  • Cloudera Data Platform Generalist
  • Data Science Council of America (DASCA) Associate Big Data Engineer
  • 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

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.

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