What is a data engineer and what do they do? Learn about the salary and job description of a data engineer, plus key data engineer skills, roles, and responsibilities in this online guide.
Here’s what we’ll cover:
Data engineers build data pipelines that transform raw, unstructured data into formats data scientists can use for analysis. They are responsible for creating and maintaining the analytics infrastructure that enables almost every other data function. This includes architectures such as databases, servers, and large-scale processing systems.
Data engineers are in charge of ETL (Extract, Transform, Load) processes in data warehouses. This involves extracting data from various data source systems, transforming it into the staging area, and loading it into the data warehouse system.
To do this, data engineers need an in-depth knowledge of SQL and other database solutions such as Cassandra and Bigtable. If a company starts generating large amounts of data from different sources, a data engineer’s job is to organize the collection, process it, and store the information.
Data engineers earn an average salary of $127,983, according to Indeed. Top companies for data engineers include Netflix, Facebook, Target, and Capital One. An entry-level data engineer with less than one year of experience can expect to earn $77,361, including tips, bonus, and overtime pay, according to Payscale.
The five highest-paying cities for data engineers are as follows:
The job description of a data engineer usually contains clues on what programming languages a data engineer needs to know, the company’s preferred data storage solutions, and some context on the teams the data engineer will work with. The level of skills and the foundational knowledge required varies widely from junior data engineering job descriptions to senior data engineering job descriptions.
Potential data engineering candidates will be expected to:
Data engineers need to be literate in programming languages used for statistical modeling and analysis, data warehousing solutions, and building data pipelines, as well as possess a strong foundation in software engineering.
While data engineers job specs will vary across different industries, most hiring managers focus on:
Since data engineers can come from different educational backgrounds, soft skills are also important to many employers. The following skills are useful to have when competing for a data engineering role:
Data engineer job profiles vary widely between companies. The scope of these roles depends largely on the size of the company, the maturity of its data operations, and the volume of data collected.
Data engineers are responsible for building and maintaining an organization’s data infrastructure, including databases, data warehouses, and data pipelines. A typical data engineer profile requires the transformation of data into a format that is useful for analysis.
This starts with cleaning, organizing, and processing raw, unstructured data. Data pipelines refer to the design of systems for processing and storing data. These systems capture, cleanse, transform and route data to destination systems, taking raw data from a SaaS platform such as a CRM system or email marketing tool and storing it in a data warehouse so it can be analyzed using analytics and business intelligence tools.
To better understand the work of a data engineer, consider the back-end database structure for a mobile app service like Uber. A data engineer is responsible for building and maintaining this system in its entirety, which consists of:
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.
Download our guide to data science jobs
Learn everything you need to know about data science careers in this comprehensive guide
Ready to learn more?
Browse our Career Tracks and find the perfect fit