6 responsibilities of a Data Engineer

The Data Engineer position is relatively new and because of this, there is a big difference in the responsibilities assigned between companies. Do you really know the responsibilities of a Data Engineer? Do you think it is complicated to describe the tasks performed by a Data Engineer?

If any of your answers is no then you have to read this post in which we will review the 6 main responsibilities of a Data Engineer.


Among the responsibilities we can list –

  • Move data between systems
  • Manage the data warehouse
  • Build and manage data pipelines
  • Make data available to end users
  • Carry out the company’s data strategy
  • Deployment of ML models to production environments


1- Move data between systems

This is the main responsibility of a Data Engineer.

Extract data from multiple sources such as external APIs, databases, flat files, cloud storage (S3, Azure Storage), etc.

Transformation –
It is about transforming the data in order to filter it, enrich it, aggregate it, change its structure.

Loading –
In this step the data is loaded into the final database where it will be consumed by other systems. This base can be a data warehouse, cloud storage, in-memory databases, etc.


2- Manage the data warehouse

More and more companies are beginning to use data warehouses in their data architecture. Here the responsibilities of the Data Engineers are:

Data warehouse modeling –
To model data in such a way that analytical queries take less time.

Data warehouse performance –
To ensure that queries run quickly and ensure that the warehouse can scale without performance degradation as the amount of data increases.

Data quality –
To ensure that the quality of the data is adequate.


3- Build and manage Data Pipelines

It involves:

  • Moving data between systems, between databases, between warehouses, etc.
  • Transforming: the data between formats, make aggregations, etc.
  • Monitoring data pipelines
  • Managing metadata

Some programs used for this purpose are: Airflow, Prefect, Dagster, AWS Glue, AWS Lambda, Data Factory.


4- Make data available to end users

With the data available in the data warehouse, it’s time to make it available to end users. The end users can be analysts, applications, external clients, etc. Depending on the end user, you must configure:

Reports/Dashboards –
These are platforms used to analyse data graphically and intuitively. Some of these platforms can be: Tableau, Metabase, Superset, Power BI.

Access permissions –
It is necessary to generate permissions for users and applications for a table.

Endpoints (APIs) –
Some external applications/clients may need access through an API to consult the information.

Data dump for clients –
Some clients may require specific extractions of information. In those cases, the Data Engineer must generate the necessary pipelines to make these extractions available.


5- Carry out the company’s data strategy

This includes:

  • Decide what data to collect, how to collect it, and how to store it securely
  • Lead the evolution of data architecture to meet new information needs
  • Educate end users on how to use data effectively
  • Decide what data to share with end users


6- Deployment of ML models to production environments

Data scientists build models that accurately predict the behaviour of certain business processes. The Data Engineer will be able to optimise them for their use in a productive environment.



In this article we discussed the main tasks that are a responsibility of Data Engineers. It is important to keep in mind that the tasks that a Data Engineer must fulfil vary according to the company, the structure of the team and the workload. Although, broadly,  the main task of a Data Engineer is to make data available for decision making.


What do you think about this? Have you worked or would you like to work in a Data Engineer position?

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