Cloud Engineer Course

The Cloud Engineer, a rapidly growing profession that emerged in the 2010s, bridges the gap between traditional IT and software engineering. This makes them critical for organizations building and managing cloud infrastructure.

Cloud Engineers specialize in designing, developing, and maintaining cloud-based systems. They ensure efficient and secure storage, processing, and access to data and applications. They also implement best practices for automation and infrastructure management.

Compared to traditional IT roles, Cloud Engineers collaborate closely with developers and operations teams to design and implement scalable, cost-effective cloud solutions.

With their expertise in cloud platforms and infrastructure, Cloud Engineers, sometimes called “CEs,” are essential for optimizing an organization’s cloud environment. They ensure smooth operation, high availability, and efficient resource utilization to support business objectives.

While traditional IT professionals focus on maintaining on-premise infrastructure, Cloud Engineers specialize in building and managing cloud-based systems. Their expertise ensures efficient, secure, and scalable solutions. On the other hand, traditional Data Engineers “only” focus on building data pipelines and can greatly benefit from Devops practices.  Here’s a closer look at a Cloud Engineer’s skillset:

– Cloud Architecture and Design: Cloud Engineers design and implement secure, scalable cloud architectures that meet business needs. They understand the strengths and weaknesses of different cloud platforms (AWS, Azure, GCP etc.) to choose the best fit.

– Infrastructure Management and Automation: They provision and manage cloud resources like storage, compute instances, and networking. Additionally, they automate tasks using scripting languages and infrastructure-as-code tools to improve efficiency and reduce errors.

– Security and Compliance: Cloud Engineers prioritize security by implementing access controls, encryption methods, and intrusion detection systems to safeguard data and resources in the cloud. They also ensure compliance with relevant data privacy regulations.

– Networking and Connectivity: They configure and manage virtual networks within the cloud environment, ensuring secure and reliable communication between cloud resources and on-premise infrastructure.

– Monitoring and Troubleshooting: Cloud Engineers continuously monitor cloud resources for performance issues and proactively troubleshoot problems. They use various monitoring tools to identify and resolve potential bottlenecks.

– DevOps and Collaboration: They collaborate with developers and operations teams to integrate cloud infrastructure with development workflows using DevOps principles. This enables faster development cycles and smoother deployments.

– Continuous Learning: The cloud computing landscape is constantly evolving. Cloud Engineers stay up-to-date with the latest technologies and advancements through ongoing learning and certification programs.

Just like having a toolbox is essential for a carpenter, a Cloud Engineer needs a specific set of skills to excel. These skills encompass technical expertise, problem-solving abilities, and a strong understanding of cloud concepts. Here’s a breakdown of the in-demand skills for Cloud Engineers:

– Cloud Platform Expertise: In-depth knowledge of major cloud platforms like AWS, Microsoft Azure, or Google Cloud Platform (GCP) is crucial. This includes understanding their core services, pricing structures, and best practices for deployment and management.

– Infrastructure and Automation: Cloud Engineers need strong skills in provisioning and managing cloud resources like storage, compute instances, and virtual networks. Additionally, they should be adept at automating tasks using infrastructure-as-code tools and scripting languages to improve efficiency and minimize errors.

– Networking and Security: A solid understanding of network concepts is essential for configuring and managing secure virtual networks within the cloud. Cloud Engineers also implement security measures like access controls, encryption, and intrusion detection systems to safeguard data and resources.

– DevOps and Collaboration: The ability to collaborate effectively with developers and operations teams is key. Cloud Engineers should understand DevOps principles and practices to integrate cloud infrastructure seamlessly with development workflows, enabling faster deployments and smoother operations.

– Problem-Solving and Analytical Thinking: Cloud environments are dynamic, and issues can arise. Cloud Engineers need strong analytical and problem-solving skills to diagnose and troubleshoot problems efficiently.

– Communication Skills: The ability to explain complex technical concepts in clear and concise language is essential. Cloud Engineers collaborate with various stakeholders, and clear communication is key to successful project execution.
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The training consists in a total of 700 hours of training, of which 200 hours are allocated to projects, 85% of your training takes place on a personalized coaching platform, while the remaining 15% is in the form of masterclasses, where an experienced teacher leads a course and answers all your questions. The curriculum is made up of 2 modules: Data Engineer & DataOps.

The 200 hours to be allocated to projects are broken down as follows:

– Data Engineer project: 120 hours
– DataOps project: 80 hours

The Cloud Engineer course enables you to choose a training schedule to suit your needs:
– Bootcamp format, intensive schedule of 35/40h per week for 7 months
– Part-time format requiring involvement of 10h per week for 16 months.

Book an appointment to find out more

Assessment of results is made through the implementation of an assessment procedure to determine whether the learner has acquired the skills required for the role of Cloud Engineer

There are two aspects assessed by the pedagogical team:

– Projects to put the learner in a professional situation
– Online practical cases to progressively apply your theoretical learning.
 
Finally, online assessments are hand-corrected by our panel of qualified teachers: everything is done to ensure that each learner can progress efficiently and at his or her own pace. At DataScientest, we’re convinced that only personalized follow-up ensures quality learning!

Throughout your training, and as your skills are developed, you will carry out several projects in groups, according to the breakdown of the curriculum:

Module: Data Engineer
Project: Development of a data pipeline.

Module: DataOps
Project: Cloud-based & devops approach to deploying apps. 

These projects can be drawn from our catalog, which includes a wide range of subjects based on technical business issues. You can also propose your own projects, as long as the data is accessible and our teaching team validates them.

This is an extremely effective way of putting theory into practice and ensuring that you apply the topics covered in class.

These projects are highly appreciated by companies, as they ensure the quality of the training and the knowledge acquired at the end of the Cloud Engineer course, since the use of soft-skills is also very present. These projects will teach you to:

– transmit information ;
– present and popularize your work;
– highlighting data with interactive tools (Dashboard, Streamlit…).

In short, these projects will require a real investment, representing at least a third of your training time.

The 200 hours to be allocated to the projects that make up the curriculum can be broken down as follows:

– Data Engineer project: 120hours;
– Dataops project: 80h 
The projects are supervised by DataScientest mentors who will be in regular contact with you to monitor your progress and provide guidance.

As the B2B leader in data science training, DataScientest enjoys a high level of recognition among the companies that entrust us with the data science training of their teams. This trust forges a fortiori the recognition of its diplomas.

If you’d like to strengthen your skills, DataScientest has set up a number of expert courses and publisher certifications (AWS or Microsoft Azure) to help you deepen your knowledge and perfect your data skills.

As for the Data Scientist, Data Analyst or Data Engineer, the salary to which a Machine Learning Engineer can claim varies according to his experience, the company that hires him and the city of exercise of his professional activity.

On average, a Junior Machine Learning Engineer can earn between €35,000 and €40,000/year . The salary of an expert can go up to 60,000€/year . The average salary in France is €40,000 per year, while it can exceed one hundred thousand euros in the United States!

The demand for work and therefore the job offer in AI and in particular in Machine Learning Engineering is booming. The Machine Learning labor market is even currently in short supply. Companies are becoming more and more aware of the added value of Machine Learning to take full and more effective advantage of their data and are struggling to find the right profiles. This opens the doors even more to candidates and puts upward pressure on salaries!

Today there are hardly any sectors that do not compete for talent. The applications of Machine Learning affect the fields of education as well as health, industry, IT, etc. Moreover, they are as varied as the data itself: image and speech recognition, customer knowledge, risk management and fraud prevention.

To enroll in the DevOps training program, Applicants are expected to demonstrate an understanding of programming languages and if possible Linux systems.

Applicants who do not have the required level of qualification may be granted an exemption based on their application and a written test.

To follow the course, learners must have a computer with an Internet connection and a webcam.

On the first day of your entry into training, a platform dedicated to career services containing all the workshops essential to your job search will be presented to you.

You can access it continuously, even after the end of your training.

Estelle and Vincent, our career managers, are entirely dedicated to you throughout your training. It is possible to make an appointment individually with one of them in order to accompany you and answer any questions you may have about your career plan.

In addition to this, career workshops are organized every month:

– A workshop to help you write a good CV and data-oriented LinkedIn
– A workshop to help you strategize your job search with different topics on presentation, career change, salary negotiation and technical test training.
 
In addition to these subjects, there are other workshops to be defined according to individual needs. On the other hand, concrete actions are implemented to support you in your job search: recruitment fair organized by DataScientest with its partner companies, organization of Webinars with data experts, communication actions to boost your visibility (CV competition, DataDays, project articles published on the blog and external reference media). 

To find out about all of DataScientest’s career support actions, click on this link.

Beta tests are available for our alumni in order to gain data knowledge even after the end of the training. 

In parallel, newsletters drawn up by our data scientists are regularly sent and are a reliable source of specialized information in data science. 

Finally, the DataScientest community continues to grow, and with it all of its alumni. To keep in touch and allow former students to communicate with each other, DataScientest has set up a  group of alumni on LinkedIn  who share and discuss various themes around Data Science.

The  DatAlumni community  is a  LinkedIn community  that brings together DataScientest alumni. On this page, questions, tips and technology news are shared for everyone’s benefit. 

In addition to this, DataScientest will launch in the coming weeks a trombinoscope which will put alumni in contact, this one will include the company and the position of each one.

Initially, DataScientest supported the data transition of companies . This has made it possible to create strong links between the major groups which have ensured the growth of our structure.
 
Subsequently, they are the ones who motivated the launch of our offer to individuals in order to compensate for the lack of competent profiles. This need for good profiles is reflected in the survey we conducted among 30 CAC 40 groups . Even if they had tight budget constraints, only 4% believe they would downsize their data scientist workforce; by comparison, 28% would still seek to increase their number by more than 20%.

On the strength of our past with large companies, we then signed partnerships linked to the hiring of our alumni . All the partner companies undertake to include all our students at the end of their training in their recruitment process : this, coupled with help with CVs and interviews, means that you will be in pole position to land the job of your dreams!

With our experience with large companies, we regularly organize recruitment fairs with our partner companies, addressed to all our students and alumni.