Responsibilities of data engineers

The clients who rely on data engineers are as diverse as the skills and outputs of the data engineer teams themselves. No matter what field you work in, your clients will always determine what problems you solve for them and how you solve them.

Here, through the prism of data needs, we list several typical customer groups: what is IT?

Data Science and AI Teams;

Business intelligence or analytics teams;

Product teams.

Before any of these commands can work effectively, the data must meet certain requirements and provide:

Confident transition to broader analytics systems;

Be available to all participants in the processing and research processes.

These requirements are detailed in Monica Rogarty's excellent article “The AI ​​Hierarchy of Needs". As a data scientist, you are responsible for meeting your customers' needs for quality data. However, you will use different approaches to meet the individual requirements of your data mining workflow.

Data stream

To do anything with data on the system, you must first ensure that it can enter and pass through the system securely. Input data can be almost any data type that you can imagine, for example:

Data engineers are often responsible for using this data, designing a system that can take this data as input from one or more sources, transform it, and then store it for their clients. These systems are often referred to as ETL pipelines એ which stands for extract , transform, and load .

Responsibility for the data flow is mainly in the extraction phase . But the data engineer’s responsibility is not limited to loading them into the pipeline. They must ensure that the pipeline is robust enough to withstand unexpected or corrupted data, disconnection of sources on fatal errors. Uptime is very important, especially when you are using real-time data or data that is time dependent.

Your responsibility to maintain the flow of data is constant and independent of who your client is. However, some clients can be more demanding than others, especially when the client is an application that relies solely on real-time data.