What is the Difference Between a Data Architect and a Data Engineer?

The growing importance of data in organizations undergoing digital transformation is redefining the roles and missions of data-driven people within the organization. Among these key profiles are the Data Architect and the Data Engineer. For most people, both of these functions are unclear: although their roles can seem quite similar, their purposes and missions are quite different. 

Because enhancing data is a complex task, organizations must work with the right people: specialists who can create a data-driven culture. It is recommended to hire a Data Architect and a Data Engineer within the data department. Although these two key roles overlap and often lead to confusion, they each fulfill different missions. To know whether or not you should hire a Data Architect or a Data Engineer (or both), it is important to understand their scopes of work to create data synergy.

The Wide Range of Skills of a Data Architect

A Data Architect’s main mission is to organize all the data available within the organization. To do so, they must be able to not only identify and map the data but also prioritize it according to its value, volume, and criticality. Researching, identifying, mapping, prioritizing, segmenting data…the work of a Data Architect is complex and these profiles are particularly sought after. And for good reason. Once this inventory of data has been completed, the Data Architect can define a master plan to rationalize the organization of the data.

A Data Architect intervenes in the first phases of a data project and must therefore lay the foundations for exploiting data in a company. As such, they are an essential link in the value chain of your data teams. Their work is then used by data analysts, data scientists, and, ultimately, by all your employees.

What are the Essential Skills of a Data Engineer?

A Data Engineer follows a Data Architect in this vast task of creating the framework for researching and retrieving data. How do they do this? With their ability to understand and decipher the strengths and weaknesses of the organization’s data sources. As a true field player, they are a key to identifying enterprise-wide data assets. Highly qualified, a Data Engineer is an essential part of a data-driven project.

If a Data Architect designs the organization of the data, the Data Engineer ensures its management, the respect of good practices in the processing, the modeling and storage on a daily basis. Within the framework of their missions, a Data Engineer must constantly ensure that all of the processes linked to the exploitation of data in an organization are fluid. In other words, a Data Engineer guarantees the quality and relevance of the data, while using the framework defined by the Data Architect with whom they must act in concert.

Data Architect vs. Data Engineer: Similar…but Above All, Complementary

A Data Architect and a Data Engineer often follow similar training and have comparable skills in IT development and data exploitation. However, a Data Architect, with their experience in database technology, brings a different value to your data project. With more conceptual contributions, a Data Architect needs to rely on the concrete vision of a Data Engineer. The combination of these key profiles will allow you fully exploit enterprise data. Indeed, a Data Architect and a Data Engineer work together to conceptualize, visualize, and build a framework for managing data.

This perfect duo will allow any organization to maximize its data projects success and above all, create conditions for a sustainable, rational and ROI-driven exploitation of your data.

About Actian Corporation

Actian makes data easy. Our data platform simplifies how people connect, manage, and analyze data across cloud, hybrid, and on-premises environments. With decades of experience in data management and analytics, Actian delivers high-performance solutions that empower businesses to make data-driven decisions. Actian is recognized by leading analysts and has received industry awards for performance and innovation. Our teams share proven use cases at conferences (e.g., Strata Data) and contribute to open-source projects. On the Actian blog, we cover topics ranging from real-time data ingestion, data analytics, data governance, data management, data quality, data intelligence to AI-driven analytics.