What Job Roles Does a Data Engineer Perform?

Data engineer job roles vary across different industries and companies. Explore the different types of roles fulfilled by data engineers in this guide.

Here’s what we’ll cover:

Data engineers form the backbone of an organization’s data ecosystem. They build data pipelines that enable data scientists to perform exploratory analysis and unearth conclusions from data that drive high-impact business decisions.

Because they are further away from the end product, data engineers tend to receive less recognition than data scientists.

Learn more about how to become a data engineer here.

data engineer job roles

What Roles Does a Data Engineer Perform?

In smaller companies, data engineers may perform the role of a full-stack data scientist, which includes data analysis and reporting. In a large enterprise, data engineers work alongside data scientists to build data pipelines that enable data scientists to query data stored in data warehouses.

Below are some typical data engineer roles.

  • Generalist. A data engineer on a small team may be responsible for every step of data flow, from configuring data sources to managing analytical tools. In other words, they would architect, build and manage databases, data pipelines, and data warehouses. A generalist data engineer might not possess the advanced statistical modeling skills of a specialized data scientist, but they should know enough about data analysis to perform basic analytics and reporting functions.
  • Pipeline-centric. Adata engineer’s role in a mid-sized company involves working side by side with data scientists to build whatever custom tools they need to accomplish certain big data analytics goals. They oversee data integration tools that connect data sources to a data warehouse. These pipelines either simply transfer information from one place to another or carry out more specific tasks. For example, a data pipeline may include data staging areas, a temporary storage area between a data source and a data warehouse. The staging area is used to combine data from multiple sources.
  • Database-centric. In a large enterprise with highly complex data needs, data engineers may focus on setting up and populating analytics databases, tuning them for fast analysis, and creating table schemas. This involves ETL (Extract, Transfer, Load) work, which refers to how data is taken (extracted) from a source, converted (transformed) into a format that can be analyzed and stored (loaded) into a data warehouse.

data engineer database centric

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.

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