Data Engineer job description: Salary, training, missions and skills

Data Engineer job description: Salary, training, missions and skills

Interested in Jedha's training courses?
See the Jedha syllabus
Our latest articles!

As companies understand the importance of data, the need to organize and structure data infrastructures emerges. If the infrastructure makes it easy to extract and analyze data, companies can more simply add value to it. For example, it becomes possible to apply Machine Learning algorithms, to create relevant dashboards or to optimize storage costs. Therefore, the job of Data Engineer has become crucial and that is why we offer a Data Engineer training at Jedha. Let's talk in more detail about this job, the salary you can expect as a Data Engineer, the training, the missions, the skills and the perspectives of evolution. 

What is a Data Engineer?

Description of the job

The objective of the Data Engineer is to create and optimize Data infrastructures so that business teams or Data Analysts and Data Scientists can use the available data easily.

Although it may seem simple at first glance, the infrastructures become increasingly complex to set up as the company grows. This is because it is necessary to ensure that the data flows easily but also that it respects confidentiality standards.

Job File Data Engineer
Job File Data Engineer

What are the missions of a Data Engineer?

The Big Data engineer works on everything related to the Data infrastructureof a company. More precisely, he will be found on missions such as :

  • Industrialization / deployment / monitoring of Machine Learning (ML) algorithms developed by the team of Data Scientists;
  • Centralization and standardization of data in a Data Lake;
  • Design and implementation of scalable data processing pipelines (ETL / ELT), very often integrating the Big Data dimension (Spark);
  • Monitoring of different data flows.

As you can see, although the Data Engineer job has a structural Data dimension, there is a strong technical component that should attract you if you want to do this job. 

What you need to master is at the heart of the Data Engineer's job: the ETL process. This acronym stands for "Extract Transform Load". It corresponds to the method by which you can extract data (very often from your Data Lake), transform it and load it into a database called Data Warehouse.

What is the role of the Data Engineer in a Data project?

If we were to summarize Data projects, we would describe them as a pipeline with several steps. The following steps can be found in a Data project:

  • Data collection: You will have to find the data you need for your analyses, which may be in databases or you may have to scrape them off the web, for example!
  • Exploration: Once you have the data, you will have to clean and prepare it. To do this, an exploratory phase is often carried out, known as EDA (Exploratory Data Analysis). The objective is to detect outliers or missing values, for example. 
  • Exploitation: Now that everything is ready, you can use the data for the objective that has been set. For example, in Data Science, you will develop a Machine Learning algorithm. 
  • Production: Once you have successfully developed your AI algorithm or solution, you will need to put it into production at enterprise level. This involves different technologies and is a separate step.

As a Data Engineer, you will very often be involved in the Collection and Production phases. Indeed, it is at these two stages that the infrastructure part is most often found.

  • Collection: data must be easily stored in a data lake, which is often built on cloud platforms such as AWS.
  • Production: you will have to send the algorithms built on servers, and therefore touch the company's infrastructure.  

What is the difference between a Data Engineer and a Data Scientist?

As explained above, the Data Engineer focuses on the Collective and Production part. Their role is to maintain a robust data infrastructure, no matter how big or small, but also to ensure that the results of the AI models built by the Data Scientists are made available to the right stakeholders.

The Data Scientist must build solid machine learning models from the company's data in order to guide future strategic actions, and thus respond to internal or external business issues.

These are therefore professions with different missions and skills, to say the least, but whose actions will allow both professions to work together. Which job should you choose in the field of data? We help you to see more clearly by giving you all the keys for both professions: Data Scientist vs Data Engineer.

Data Engineer Job Guide

The skills required

To become a Data Engineer, your arsenal of skills must be primarily technical. For example, programming languages should hold no secrets for you. Knowledge of one or more cloud platforms and environment standardisation tools is recommended. Here are a few points on which we advise you to focus: 

  • Python programming: This is the flagship language in Data that you will need to know to get ahead. It's what you'll use all the time in your work as it's the foundation on which the technologies around data analysis are built. 
  • Cloud: Cloud computing has become an essential element in the structuring of companies. Of all the cloud platforms, the three best known are Amazon Web Services, Microsoft Azure and Google Cloud Platform. AWS is still by far the most widely used platform with over 30% of the public cloud market share, so this is where we recommend you start.
  • Machine Learning: You will sometimes put Machine Learning algorithms into production. Without having a deep experience in the field, it is therefore important that you know what it is and how these algorithms work. 
  • SQL database management: When collecting data, you will need to use SQL. As a Data Engineer, you will need to master the language but also know the types of databases that hold all this data. For example, you will need to know the difference between row-oriented and column-oriented databases.  
  • Environments & Production: Infrastructures are diverse today and that is why it is important to standardise environments as much as possible. For this, there are tools like Docker & Kubernetes, which are important to know. You will use these tools combined with those specific to the production of Machine Learning algorithms such as MLFlow or AWS SageMaker to scale your Data infrastructure.
  • Big Data: It is now essential that an infrastructure can accommodate Big Data. That's why you need to know specific languages like Scala to be able to use Spark to create robust ETLs regardless of the volume of data.

The salary 

Depending on the type of company and the sector, salaries vary, but here is an average salary scale:

Position Average salary
Junior Data Engineer 40 000€ - 48 000€
Data Engineer 40 000€ - 62 000€
Lead Data Engineer 60 000€ - 83 000€

Source - Glassdoor & Jedha Alumni Studies

What is the daily rate for a Data Engineer?

The mdt of a Data Engineer (average daily rate) for 2023 is 445 €, or 63 € / hour.

How much does a freelance data engineer earn?

Junior (entry-level) data engineer positions start with an average salary of €45,000 per year, while the most experienced ones can earn up to €111,000 per year.

Data Engineering courses: becoming a Data Engineer

Big data engineering training courses are rather scarce, but there are still a few to choose from. Here are some ideas for you:

  • Online courses: They are great for getting started and training at your own pace, but it is difficult to gain credibility in the market with only online training. 
  • Masters in Data: you will be able to have a more complete training and a state-recognised diploma. However, you will need to invest between €10,000 and €20,000 and at least one year of your time. 
  • Intensive Bootcamp training: It is the right compromise between the flexibility of an online course and the theoretical depth of a master. Data bootcamps have become a real alternative to classical training because they are very practical and teach you skills directly applicable in companies. Don't hesitate to have a look at our Data Engineer intensive training courses, ranked as the best Data bootcamp training in France! Jedha offers an advanced Data Engineering training, allowing you to have a solid foundation in Reinforcement Learning. You can take advantage of this training on a part-time or full-time basis, in person or remotely.

Depending on your background, your initial level and your aspirations, you will opt for one type of training rather than another. We have written a whole article to help you choose the best Data training for you. Don't hesitate to have a look at it. 

What are the career prospects of a Data Engineer?

When you start your career in Data, you will find that there are many opportunities open to you. Once you have gained experience as a Data Engineer, you can aspire to positions of greater responsibility: 

  • Lead Data Engineer 
  • Chief Data Officer 
  • Chief Technology Officer

You can also make a more horizontal progression with trades such as :

  • Data Scientist
  • Machine Learning Engineer
  • Software Engineer

How to become a Data Engineer?

If you want to start in Data and are interested in becoming a Data Engineer, here are some tips that may help you: 

  • Do projects: if you don't have work experience, that's okay! You can gain experience by doing open source projects. To do this, you need to build a robust data infrastructure. Without investing astronomical sums, you can still build a portfolio by building ETL pipelines or data lakes etc.
  • Highlightyour background: If you have experience in other fields, it is important to highlight this. For example, you may have experience in marketing or finance and want to change careers. All your past experience is valuable and it will give you a "business" advantage over other candidates who only have technical skills. 
  • Gain experience: The Data Engineer job is often open to people who already have some experience in Data. Simply because it requires a very cross-functional knowledge. However, you can start out as a Data Scientist and work your way up in technical areas, for example, or you can be a software engineer and acquire this Data dimension. What is important is to start with a first opportunity and build your career path as you go along. 


The profession of big data engineer is a rare commodity, and is therefore sought after by companies. In order to stand out during your interviews, don't hesitate to complete your portfolio: projects carried out during your training, personal projects or your previous jobs.

If you would like to know more, please contact our admissions team by making an appointment directly on our website.

Frequently asked questions about the Data Engineer job

Why work in data?

"Tech positions are evolving far too quickly to train in just one role. At Jedha we teach our students not singular jobs but total domain expertise. This is our absolute priority for making their learning experience enduring".

Antoine Krajnc - Founder of Jedha

What are the most common questions I get when interviewing to become a Data Engineer?

During your interviews for a Big Data Engineer position, you will be asked to bring your resume, cover letter and portfolio of your Tech projects. Most companies also ask you to take technical tests to evaluate your data engineering skills, but also your logic and your ability to adapt.

Alizé Turpin
Written by
Alizé Turpin
Director of Admissions