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

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.

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.