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Data refers to the digital information that customers, consumers, and members create.

The data engineer is the first to access and manage data sets.

Information, videos, photos, comments, questionnaires... Data processing is crucial to helping with a company's strategy and management. An immense resource if used properly, it is obviously linked to artificial intelligence.

Presentation and responsibilities of the Data Engineer

The data engineer, also known as a data engineer, appears at the very beginning of the data processing chain.

He is the first to approach big data that is still raw. He creates the architecture for data volumes. He is responsible for ensuring their storage, clarity, and security before passing them on to other players in the chain. For example, he seeks to identify erroneous, duplicate, invalid, or suspicious information. He detects potential malfunctions. He inspects new sources to collect new data. He connects different tools together, creating a secure architecture.

To summarize the big data chain: the data engineer collects, secures, and clarifies the data, then the data analyst studies it (from an operational and business perspective), and finally the data scientist applies algorithms to transform it into forecasts, among other things.

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Business implications

Data engineers can tackle any data stream. They are therefore able to work in many different sectors and on a variety of topics.

Large corporations seek these types of profiles: banks, laboratories, insurance companies, real estate, telecommunications, energy, etc. Sometimes in SMEs, a single role combines the professions of data engineer, data analyst, and data scientist.

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Ethical implications
The presence of data also raises issues of confidentiality and freedom. Data engineers are well versed in these topics and can also rely on data and AI lawyers and ethics officers to assist them with complex issues.
Aivancity's programs incorporate all aspects of artificial intelligence and its challenges, whether technical, technological, commercial, ethical, or legal, into their teaching. These comprehensive, hybrid courses enable future engineers to acquire the highest level of knowledge and a broad range of expertise.

Key skills

Data engineers develop data flows. They are familiar with programming languages (JavaScript, Scala, Python, etc.), database tools (SQL and NoSQL), and data management systems such as Hadoop. Their job is to lay the groundwork for smoother processing downstream.

Data engineers work in teams, so good interpersonal skills are essential. Accuracy and analytical skills are among their key qualities. They are organized, rigorous, and concise.

Trends and factors driving change

Independence or management are the two possible paths for an experienced data engineer.

It all starts with the work of the data engineer: relevant, reliable, and actionable data so that the data can reveal its full potential.