With the growth of Data Science in recent years, two professions have become highly valued within the area: the Data Engineer and the Data Architect. These functions are responsible for assisting in the execution of projects that use Data Science to generate valuable insights for organizations.
But do you know the difference between them?
In this article, you will understand once and for all what each Data professional does – and what you need to do to become one of them.
Understanding Data Science and its Professions
To better understand what a Data Engineer and Architect does, we first need to have an overview of Data Science .
After all, what is Data Science?
As the name implies, it is a multidisciplinary area that works with data organization and interpretation . In other words, it seeks to capture, process, transform and analyze data to generate strategic insights and specific results for companies.
In recent years, Data Science has emerged as one of the most promising professions in the tech market . Although this rise is recent, it is a discipline that has been around for a long time – it was not created by the technological revolution of the last decade. Data Science has always been present in studies involving quantitative analysis, whether in the humanities, biological or exact areas.
What has really changed recently and made Data Science a professional career is the sheer volume of data available for analysis and the competitive intelligence it can provide for organizations.
With the advent of the digital age and the constant advancement of technology, immense amounts of information are generated daily from various sources, such as social networks, commercial transactions, mobile devices and smart sensors. This explosion of data created the need for specialized professionals , capable of assembling complex structures for analysis and extracting valuable information.
The day-to-day work of this profession requires a lot of dedication and attention to detail, as it deals with a huge amount of data of different types. To do a good job it is not enough to deduce, it is necessary to understand the whole context in which the data are inserted and how to interpret them in a way that they contribute to generate solutions, actions or improvements .
Thus, in their routine, the data professional can find patterns that identify a certain behavior or a possible trend for the market, anticipating actions so that they occur within criteria that are beneficial to the organization.
To do all this, Data Science uses computational and mathematical methods , as well as statistics and optimization to process and analyze data, exploring and predicting user behavior to generate strategic insights.
When we talk about the professional who is part of this Science, we are talking about someone who understands several different functions. Overall, the most common profession is working as a Data Analyst or Data Scientist , but there are other careers in the field, such as the Engineer and the Data Architect.
Next, let’s better understand the basic differences between the main Data Science professionals:
1. Data Architect
He is responsible for corporate design and the design of the organization’s overall data structure . This includes defining data models, identifying storage needs, and defining data governance strategies. They take a broader view of data requirements and work collaboratively with multiple industries to ensure the data architecture meets business needs.
2. Data Engineer
He is responsible for designing, building and maintaining the company’s data infrastructure. They are experts in data storage, processing and integration technologies. His work involves building data pipelines, optimizing queries, and ensuring data integrity. All this so that they are available, consistent and can be accessed efficiently for analysis and decision making .
3. Data Analyst and Scientist
It is the professionals who will do the data analysis. Its main objective is to extract meaningful insights from data and transform them into actionable information. Data scientists apply statistical techniques, machine learning algorithms and other analytical tools to solve problems and make evidence-based decisions. Analysts are responsible for transforming data into strategic insights for the company.
What are the main differences between the Engineer and the Data Architect?
It is common for people to confuse the two professions. Although both are directly involved in the field of Data Science and Big Data, the roles they play are not the same – although many activities overlap.
The Data Engineer is responsible for transforming raw data in different formats, such as text or graphics, into information that can be analyzed and worked on by another professional. Just like a traditional engineer, he is the one who will design, build and maintain the data infrastructures in an operational way.
On the other hand, a Data Architect has a broader and more strategic vision, reconciling business requirements with the technical and operational part of data infrastructures. It also defines data governance policies and standards, as well as the appropriate tools and technologies for each type of data.
Is it possible for a single professional to work as an Engineer and Data Architect?
Yes, it is possible. In fact, it is quite common for smaller companies to have only one professional performing both functions. In larger companies, where the data policy is complex and elaborate, the roles are usually performed by two different professionals.
In the career, the natural path is for a Data Engineer, due to having a very technical knowledge, to evolve to act as a Data Architect, adding the strategic and organizational part to his technical baggage.
