Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data. It’s about the collection and usage of data. Data scientists and data analysts work with data and the data engineer is responsible to ensuring that the data is reliable and easily accessible. Data engineers are involved with ETL, which stands for extraction, transform and load.

To understand what data engineering is, have a look at the data engineering lifecycle.

Data engineering sits upstream from data science (and data analytics). Data engineers provide the inputs used by data science. Data science and data analytics sits downstream from data engineering.

Data engineering isn’t generally an entry-level role. Instead, many data engineers start off as software engineers or business intelligence analysts.

Data engineers build data warehouses to empower data-driven decisions. Data engineering lays the foundation for real-world data science application. Data engineering requires a broad set of skills ranging from programming to database design and system architecture. Data engineers are focused on providing the right kind of data at the right time. A good data engineer will anticipate data scientists’ questions and how they might want to present data. Data engineers ensure that the most pertinent data is reliable, transformed, and ready to use. Creating the best system architecture depends on a data engineer’s ability to shape and maintain data pipelines. data engineers care most about how company data is presented, how it scales, how secure it is, and how easy it is to change data pipelines based on new information.

The data engineer has several technical skills, along with people skills. The technical skills include SQL, Python, Amazon Web Services, Azure, Spark, Java, Scala, Kafla and many others.