Data Engineering

Implementation of multi-cell ETL processes for analytical developments in automotive holding companies

The challenges

A leading automotive company with presence in several countries in the region wanted to implement improvements in its vehicle sales processes, for which it sought to use analytical models.

The reality of the company needed to automate and standardize certain processes, so we developed data ingestion, integration and consolidation processes. In addition, the data was used for experimentation with different models, supporting multiple agile cells.

The team joined three different cells where we work together and collaboratively for different countries

The strategy

Generation of a common understanding

Meetings with key people in each process to agree on a shared vision of flows and uses.

Information flows and requirements

Survey of reports, financial information (EERR, sales, purchases, etc.) to be provided by the area, their data sources, periodicity and contents.

Available data sources

Survey of all available data sources and tools.

Implementation of new data model

Creation of a new data model in order to automate the processes of car sales and purchases and sales forecasting.


Support in the implementation of a new platform for sales and purchase processes, creating a non-relational data model, robust and designed for future growth.


Automation of manual processes with ETL tools in the cloud that optimize the timing of business processes to ensure their relevance and timeliness.


Development of processes that allow to generate reliability in the information, to adequately safeguard the data and to generate a single source from which to obtain the data.

The achievements

The main achievements of the implementation were:

Integration with SAP and external APIs

Develop efficient, reliable, and robust non-relational and relational data models for data consumption

Implementation of ETL processes in the cloud

Reduce the time required to generate key business information and free up time for analysis and higher value-added tasks.

Creation and centralization of information in a Lake House