Data architects and data engineers are integral parts of a data team in a company and are often required to collaborate in the collection, analysis, and interpretation of data for the benefit of the company they work for. 

Although they are two roles that work with data as a common denominator, they have several differences. In this article, we will see: 

What is a data architect? 

What is a data engineer? 

What each role entails

How they differ from each other 

Let's start with two basic definitions.

What Is a Data Architect?

A data architect is a professional who deals with the design, creation, development, and management of data architecture within a company or organization. 

Their main concern is the efficient maintenance of a company's database, seeking solutions for issues related to data structure and installation. 

Through their work, data architects assist companies in organizing, retrieving, storing, and maintaining data. Now that we've seen what a data architect is, let's take a look at what a data engineer is.

What Is a Data Engineer?

A data engineer is a professional who specializes in managing large volumes of data, preparing them and building suitable systems for data collection and analysis. 

Additionally, they actively participate in creating solutions for data storage, such as data warehouses. 

Their primary focus is on creating algorithms, APIs, and data structures to represent data in an understandable format. 

Through their work, data engineers create the infrastructure for data management designed by a data architect. 

Moving on, let's delve into the key differences between these two roles.

Key Differences Between Data Architects and Data Engineers

Although both of these roles are essential and integral to a successful data team, they have some fundamental differences between them. 

Let's specifically look at where they differ so you can make an informed decision about which profession suits you better.

   Difference #1: Skills

A significant difference between a data architect and a data engineer lies in their different skill sets and areas of expertise. A data architect is involved in data mining and possesses machine learning knowledge to create scalable systems for managing big data. 

Furthermore, they have knowledge of data governance and data modeling tools such as ERWin and Visio for representing metadata. Proficiency in SQL and database management systems is also crucial, and knowledge of the popular Python language is a significant advantage in their work. 

On the other hand, a data engineer focuses on optimizing data processing and performance, specializing in the development of ETL processes and the creation and management of data warehouses and data lakes. 

They excel in programming languages like Python and Java and are familiar with tools like Hadoop, Spark, and Kafka. 

They also have knowledge of cloud platforms like AWS Glue and Azure Data Factory.

   Difference #2: Educational Background

Regarding educational background, a data engineer may have a degree in information technology (IT) or receive training in computer engineering. 

A data architect may also hold a degree in computer science, computer engineering, or a related field. 

However, they may choose a more specific direction in application architecture, data architecture, or network architecture.

   Difference #3: Salary

These two roles also differ in terms of expected salary. 

According to Glassdoor, the estimated total compensation for a data engineer is $117,953 annually in the United States, with an average salary of $98,795 per year. 

In contrast, the estimated total compensation for a data architect is $178,551 annually in the United States, with an average salary of $130,043 per year.

 It's evident that a data architect commands a higher salary, but everything depends on experience levels and the specific company.

Summing Up

In summary, we've discussed how a data engineer differs from a data architect, as these are two fields with high demand and financial rewards.

It’s a known fact that the data field offers many opportunities, and everyone can choose their profession based on their skills and unique preferences. 

So, if you are interested in the world of data, want to advance professionally, and gain practical knowledge, discover more articles on our blog!