Data Engineering: Driving efficiency and innovation

The data ecosystem has an essential importance in the Information Technology market, playing a significant strategic role. The exponential growth in the amount of information generated, along with the advancement of tools, has made the resource a highly competitive asset for companies. As a result, the need for qualified Data Engineering specialists has increased. Several industries are looking for talent who can manage, process and store large volumes of data. Faced with this scenario in evidence, we need to explore what the profession is, what are the main competencies expected of the professional and how this area can relate to others in the context of IT.

”Most product and solution advances occur within the enterprise — and few of those advances can occur today without data.” says McKinsey & Company, in its article “Demystifying data mesh“.

What is Data Engineering?

It is a sector of IT that focuses on designing, building and managing data infrastructures. This area is responsible for creating pipelines, storing and processing large volumes of information from different sources and ensuring that they are available for analysis and insights. A Data Engineering professional is responsible for moving and processing the company’s data, within any type of information repository, processing format and volume.

“We see a general movement in the market of companies seeking to extract more value from their data, aiming to truly become “data driven”. Recently, the financial sector in Brazil has stood out in this direction, mainly due to the Open Finance movement, as it was possible to observe at Febraban Tech 2023 (the largest technology and innovation event in the financial sector in Brazil)”.

– Gediel Luchetta, VP of Engineering at ilegra.

Key skills of a Data Engineering professional:

An expert in the field must possess both technical skills and interpersonal experiences to be successful in their career. Here are some of the most important expected competencies:

+++Programming languages: Having skills in programming languages such as Python, Java, Scala or R is crucial to develop pipelines and create scalable solutions. Knowledge in tools and libraries such as Apache Spark and Hadoop is also highly valued. In addition to SQL, which should come naturally to the data engineer.

+++Infrastructure and cloud knowledge: Understanding IT infrastructure concepts and knowing how to work with cloud services, such as Amazon Web Services (AWS), Google Cloud Platform (GCP) or Microsoft Azure, is essential to create scalable and elastic environments for data processing.

+++Data Modeling: It is important to have knowledge in data modeling to design efficient and scalable schemas, ensuring that the information is logically organized and optimized for queries and analysis.

+++Big Data Knowledge: Familiarity with Big Data technologies and concepts such as distributed storage, batch processing and streaming is essential for working with massive volumes of information.

+++ Knowledge in databases: Having experience in relational and non-relational databases, such as SQL Server, MySQL, PostgreSQL, MongoDB, among others, is essential. In addition, it is necessary to understand how to design, optimize and administer this bank for the efficient handling of large volumes of information.

+++Skills in ETL (Extract, Transform and Load): A Data Engineering professional must be able to design and implement efficient ETL processes to extract data from various sources, transform it into a suitable format and load it into a system of destination. And, also, knowing when it makes the most sense to invert, working on the ELT (extract, load, transform) model.

+++Analytic thinking: An expert in the field must have solid analytical skills to identify problems, propose solutions and optimize the performance of data pipelines.

Read too: Your data science project cannot succeed if you don’t invest in data engineering

How does the Data Engineering area relate to others within the IT field?

+++Data Science: Data Engineering professionals work collaboratively with data scientists to provide them with the information they need to enable predictive analysis and modeling. They are responsible for designing and implementing data flows, enabling scientists to access and process information relevant to their analyses.

+++Business Intelligence (BI): Data engineers provide the necessary infrastructure to create dashboards, reports and content analysis to support decision making. In doing so, they ensure that information is available reliably and in a timely manner to BI users.

+++Software Development: Working with software development teams to integrate and implement solutions in enterprise applications and systems is also a possibility. Together, these professionals design and maintain the back-end components needed to capture, process, and store the relevant data.

+++DevSecOps: Collaboration with DevSecOps experts is important for the deployment and ongoing maintenance of pipelines. Data engineers work closely with these teams to ensure the availability, performance, and scalability of information systems.

With their specialized technical skills, the Data Engineering professional is an indispensable figure for the organization’s data management. Through their experience, the specialist can help extract valuable insights and make informed decisions, driving innovation. Its importance is evident in a scenario where data has become essential figures for the success and competitiveness of companies.

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