The data engineer: pivotal in data infrastructure and processing
What do data engineers do? These professionals are responsible for setting up systems that ensure that data gets collected, stored and processed effectively. Data engineers collaborate closely with data scientists, information analysts, business analysts and data analysts to ensure that the required data is available for the proper purposes.
Whether it concerns processing large amounts of data, creating advanced data models or optimizing data infrastructures, data engineers provide the technical basis that enables organizations to make data-driven decisions. In this way, data engineers play a vital role in the success of data-driven projects.
These skills make a data engineer successful
Data engineers must possess both technical and problem-solving personal skills. These are the most important competences that every data engineer requires:
- Problem-solving capacity: When faced with complex data challenges, data engineers can quickly devise effective solutions.
- Analytical thinking: Recognizing patterns and structures in data helps generate valuable insights.
- Collaboration and communication: Data engineers can communicate clearly and cooperate efficiently with both colleagues and stakeholders about technical concepts.
- Pragmatic working: Finding the proper balance between technical perfection and practical applicability is essential in this profession.
- Independent and eager to learn: Data engineers adapt quickly and learn new tools and techniques continuously in order to remain up-to-date.
Essential professional knowledge for a data engineer
To be successful as a data engineer, a strong foundation in data processing and data infrastructure is essential.
Experience with scripting languages such as Python and SQL is also relevant for data transformations and setting up data-ingestion pipelines, both batch and streaming.
Finally, knowledge of databases (SQL, NoSQL, Graph) and data storage solutions such as data warehouses, data marts and data lakes is important.
Additional specializations help develop technical skills further:
- Microsoft Azure certification: Provides insight into general cloud solutions and data management within Azure, with additional knowledge of Amazon Web Services (AWS). With Microsoft Fabric (DP-600 and DP-700), Fabric data flows, pipelines and notebooks can be used to develop analysis assets.
- Big data engineering: Focuses on working with large data sets and creating scalable data models.
- Machine learning: For integrating machine learning models in data infrastructure and solutions.
- GenAI for data professionals: Using GenAI tooling, data engineers can work more productively and efficiently – e.g., when developing Python code.
In addition to technical knowledge, insight into data security and data privacy (object level, data level) and experience with DevOps pipelines is a valuable supplement.
The must-have education for data engineers
Would you like to start working as a data engineer, working on scalable, reliable data solutions? Then this series of training courses forms a solid foundation. You learn how modern data architectures are structured, how to integrate data from various sources and how to deal with large quantities of data.
- Introduction to data platforms: Gain insight into the structure of data, environments, architectures and platform choices that are vital to your work as an engineer.
- Source data integration: Learn to conjoin data from various sources intelligently and efficiently – a core task of the data engineer.
- Introduction to big data: Explore the possibilities of big data technologies and discover how to extract value from large, complex data sets.
These training courses lay the foundation for a career in data engineering – both technically and conceptually.
View all data engineering training courses at Capgemini
Capgemini Academy offers a wide range of training courses for data engineers, from beginners to advanced. With all Capgemini training courses, you receive a digital badge as proof of participation. Many courses also include an official exam and certification. Our experienced professionals are ready to share their knowledge and expertise with you!
Explore our range here:
Orientation and basic knowledge
- Introduction to data-driven working: Inexperienced working with data? Then start here. This training course explains what data-driven working is all about and why it’s essential for every organization.
- Introduction to DMBOK: Would you like to understand the entire playing field of data management? This training course provides a solid foundation for anyone who works with data; it’s based on the globally recognized DMBOK framework.
- Introduction to SQL: Learn the basics of the most popular query language in data analysis. This training course is ideal for beginners who want to work with databases.
Skills and tools
- Introduction to data platforms: Obtain insight into modern data architectures – from traditional databases to cloud solutions. This is essential training for every data engineer starting out.
- Source data integration: Learn how to conjoin data from various sources, transform it and make it available for analysis.
- Introduction to big data: Explore the world of big data: what it is, how it works, and why it has such a huge impact on modern organizations.
- Introduction to Python (optional): Python is the premiere programming language for data analysis and automation. This training course teaches you the basic principles of Python in the context of data.
- Advanced SQL: Want more depth? Then opt for the advanced training, in which you’ll master more complex queries and data manipulation.
Advanced training and certification
- DP-900 – Microsoft Azure Data Fundamentals (optional): Would you like to become familiar with the Azure ecosystem? This training course offers an accessible introduction to cloud data solutions.
- Data Engineering on Microsoft Azure: With Microsoft Fabric (DP-600 and DP-700), Fabric data flows, pipelines and notebooks can be used to develop analysis assets.
Future-focused working
- GenAI for data professionals: Do you want to remain future-proof as a data professional? Then learn to apply generative AI in your work – from automation to code generation.
Extra recommendation
- Scrum Master or Scrum Lift-Off: Do you as a data scientist wish to cooperate effectively in agile teams? Then understand the principles of Scrum, and how to work within an iterative development process.
Personal skills for data engineers
In addition to technical expertise, personal skills are also hugely important in the work of a data engineer. These training courses reinforce your communication, cooperation and effectiveness in your daily work.
Essential personal skills training courses
- Time management: Learn how to deal with time efficiently and set priorities in environments in which you’re confronted by considerable data and many projects simultaneously.
- Pyramid Principle: Develop the skill of structuring complex information clearly, logically and persuasively – essential in reports and presentations.
Important personal skills training courses:
- Systematic Working: Learn to work on data processes, projects and documentations with an overview and structure.
- Cooperating in a team: Strengthen your cooperation in multidisciplinary teams and learn to communicate effectively with various stakeholders.
Your springboard to success: Capgemini Academy
- Part of one of the largest, most innovative IT service providers in the world.
- A large range of training course offerings: available both fully online and in the classroom.
- Most training courses include certification and exams.
- Trainers with passion, didactic skills and practical experience.
- Average rating by course participants: 8.8.
Do you have any questions about the role of the data engineer, or about which training fits best for you? Please do not hesitate to contact us. We will be glad to share thoughts with you – also if you’re looking for custom courses for yourself or for your team.