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Job profile

Data Engineer (m/f/d)

Data, often referred to as “the gold of the 21st century,” is produced in massive quantities every day. In this context, organizing and managing it efficiently is crucial. Enter the Data Engineer: an essential professional for large corporations. Discover the key skills needed to become a Data Engineer, the salary range you can expect, and how this role differs from other data-related professions like that of a Data Scientist.

Looking for a Data Engineer (m/f/d) Job?

Is your passion for data unwavering? If you enjoy using your database knowledge and understanding of Big Data daily, we should discuss the next step in your career as a Data Engineer. We have the perfect job for you!

Seeking an Experienced Data Engineer (m/f/d)?

Whether it’s for Industry 4.0, IoT (Internet of Things), or the Customer Journey, as a company, you need the support of a Data Engineer to efficiently process and utilize large amounts of data, crucial for staying competitive.

Looking for an Exciting Project as a Data Engineer (m/f/d)?

Have you successfully led various projects in the Big Data field? Have you used your software skills, programming languages, and Machine Learning expertise for different clients? We help you find exciting projects.

What is a data engineer (m/f/d)?
Definition and Responsibilities

A Data Engineer ensures the smooth operation of a company’s data infrastructure by creating databases and managing existing datasets. With increasing digitalization, data-driven organizations have an escalating need for qualified IT specialists. These experts collect, process, and evaluate the most valuable “raw material” of the 21st century: data. Thus, providing large corporations with what may be their most valuable asset.

To convey these critical insights, Data Engineers (also known as data technicians) utilize advanced technological tools. They generate vast amounts of data that are stored, processed, then compiled into an analytics infrastructure before being transmitted to relevant personnel for subsequent work processes.

The job market outlook is extremely favorable since Data Engineers are needed wherever there is significant data volume. In addition to tech companies, sectors such as healthcare, engineering, automotive, e-commerce, finance, banking or insurance are also looking for well-trained data engineers.

Data Engineer (m/f/d) salary in Switzerland:
A comprehensive overview

The demand for (Big) Data Engineers is constantly increasing on the job market. This is why you can expect an attractive salary, above the Swiss average, depending on your qualifications and experience.

With experience, Data Engineers advance to senior positions. Depending on career level, company size, and location, Data Engineers earn an average of CHF 110’000.00 per year. Salaries typically range between CHF 77’000.00 and CHF 160’000.00, with the highest salaries in Zurich.
*Source: Kununu Gehaltscheck

Starting Salary for a Data Engineer (m/f/d):
What Juniors Earn in Switzerland

Even as a beginner, you are already among the well-paid professionals: as a Junior Data Engineer, you can expect a starting salary of around 77’000.- CHF per year. The industry also plays a role: starting salaries are higher in the automotive sector, while they are lower in commerce.

Most employers require a master’s degree for this position, meaning that bachelor’s degree holders can generally expect a lower starting salary.

Senior Data Engineer salary:
what experienced specialists earn in Switzerland

Your chances of earning a high salary increase with your experience. As a Senior Data Engineer, you can earn between CHF 100’000.00 and CHF 140’000.00. The location and size of the company also play an important role. As a senior executive, you can earn up to CHF 160’000.00.

Big Data Engineer (m/f/d) Salary

In the field of Big Data, salaries are similar. Initially, you can expect a salary of around 90’000.- CHF in Switzerland, which can significantly increase after a few years.

Data Engineer (m/f/d):
Tasks and Responsibilities

Data processing, also known as manipulation, is an essential task for a Data Engineer. The exact nature of this processing varies significantly from one company to another and depends on the use of the data. The most important operation is ETL (Extract, Transform, Load).

The Data Engineer must organize large amounts of data from various sources, often collected in a disorganized manner. This task, known as database engineering, involves managing, storing, and preparing data for other experts, such as data scientists and data analysts.

To accomplish these tasks, the Data Engineer uses a variety of technologies and tools, including:
  • Big-Data-Technologien like Hadoop and Apache Spark, as well as other No-SQL (non-relational) databases,
  • Cloud technologies such as AWS (Amazon Web Services) or GCE (Google Compute Engine),
  • relational databases,
  • ETL (extract, transform, load) tools

Data engineers work at the interface between hardware and data processing. They adapt algorithms and tools according to their project requirements and thus continuously generate important data. They are also responsible for setting up and monitoring the IT infrastructure as well as managing and securing the data.  

Their work forms the basis for data science activities and enables the professional use of data. Through Data Pipelines, a series of data processing elements, they ensure an automatic data flow. The data is then stored in a data warehouse, a central repository where an organization’s data from various sources is saved.

Difference Between Data Engineer (m/f/d) and Big Data Engineer (m/f/d)

Data Engineers, sometimes called Cloud Data Engineers or Big Data Engineers, perform similar activities with some nuances. Big Data involves enormous volumes of data, which primarily distinguishes these roles by the amount of data processed. Additionally, they often differ in the tools used in their work.

Data Engineer vs. Data Scientist: A comparison

While Data Engineers focus on data standardization, Data Scientists excel in interpreting chaotic data.
 

Data Engineers are responsible for developing, maintaining, and optimizing data infrastructure and pipelines. They collect and process data, storing it in various formats, databases, or text files.

Data Scientists, experts in data analysis, transform raw data into structured databases through measures such as tracking and monitoring. Their business management expertise allows them to create action recommendations based on data, thus turning Big Data into Smart Data.

Data Scientists develop and improve data analysis methods to gather relevant business information. They possess skills in mathematics, statistics, machine learning, and data visualization, and master programming languages such as Python, Julia, R, or SQL. Strong communication skills are necessary to present results and recommendations to relevant departments.

The two roles work closely together: while the Data Engineer organizes and stores the data, the Data Scientist analyzes it. Sometimes, the results are passed on to the Data Analyst for further analysis.

How to Become a Data Engineer (m/f/d)?
Education, Studies & Continuous Training

There are various paths to start a career as a Data Engineer, whether through education or a career change.

A assessment of your existing skills and knowledge is a essential first step. A university degree in STEM fields (mathematics, computer science, natural sciences, and engineering) is an excellent starting point to acquire the necessary expertise.

For aspiring Data Engineers, understanding the ETL (extract, transform, load) process, which involves data cleaning, and mastering common tools like Python is essential.

Data Engineer (m/f/d) Training in Switzerland

To date, there is no standardized training to become a Data Engineer in Switzerland. This is why this profession is often a career change. Although studies in this field are appreciated and advantageous when job hunting, specialists in business informatics, computer engineering, or statistics are also attractive candidates for data engineering positions.

After completing studies, many continuous training options are available. Depending on the course, you can learn the basics of programming, big data, databases, and automation. One option is to pursue a CAS in Data Engineering (Certificate of Applied Studies), such as at ZHAW (Zurich University of Applied Sciences).

Data Engineer (m/f/d) studies

Although data engineering is not a traditional field of study, some faculties now offer programs in this area. For example, at the Bern University of Applied Sciences (BFH), it is possible to obtain a bachelor’s degree in “Data Engineering.

For this type of study, an aptitude procedure is generally required to assess candidates’ skills and knowledge.

Besides this, many fields of study facilitate entry into the world of Big Data: business informatics, computer science, data management, computer engineering, or statistics. Students in these disciplines already acquire many of the skills needed for a career in data engineering.

After obtaining a degree, it is possible to pursue certification courses (CAS) in data engineering to further develop your skills.

Data Engineer (m/f/d): Continuous Training and Development Opportunities 

Due to the high demand for qualified professionals, there are many continuous training options for Data Engineers. For example, training on topics such as cloud computing, programming languages, big data technology, or automation. Additionally, as a Data Engineer, you have the right conditions to pursue continuous training or a career change as a data analyst. Other useful continuous training courses certify you in Big Data technologies like Hadoop, Spark, or Apache Kafka, which are appreciated by employers.

Data Engineer (m/f/d): Career Change 

Since there is no traditional curriculum for a career in this field, Data Engineers are often career changers. After studying computer science or statistics, various CAS courses are suitable for starting this career.

Interested individuals can choose from a multitude of learning formats and course content and determine whether they want to start on a fixed date or learn autonomously and flexibly. The offerings from providers such as FFHS or ZHAW are varied.

Career changers are not the only ones with immense chances of starting a promising career in this field. Even with a background in statistics, the demand is there, and you can continue to qualify as a Data Engineer through “on-the-job learning.” Additionally, various providers offer continuous training in this field.

Data engineering skills:
These skills are in demand

Data Engineers possess a set of essential technical skills, but their soft skills are also crucial. They regularly interact with other departments and clients, requiring excellent communication skills to solve problems as a team and successfully complete projects.
They must also motivate other employees with a “hands-on” mentality and proactively seek solutions and optimizations when data systems and processes do not work as expected.

In summary, Data Engineers must master the following technical and non-technical skills to perform their tasks effectively:

  • Technical understanding of big data infrastructures and technologies: This includes languages such as SQL
  • Knowledge of software, programming languages and machine learning
  • Excellent knowledge of databases
  • Understanding of the ELT (Extract, Transform, Load) process used for large data pools and in the cloud
  • Analytical skills
  • Communication skills to present analysis results
  • • Knowledge of data protection
  • Certifications in Big Data technologies such as Hadoop, Spark or Apache Kafka are an asset

Data Engineer (m/f/d) Career:
Job Market Opportunities

With the significant increase in demand for Data Engineers in recent years, job market prospects in the Big Data field are excellent.

These specialists are indispensable in many companies across all sectors, related to Industry 4.0, IoT (Internet of Things), or Customer Journey. Engineers from mechanical engineering, the automotive industry, or the chemical industry who have opted for Big Data are particularly sought after.

In general, talented Data Engineers are in demand in all large companies dealing with large amounts of data. This also includes sectors like e-commerce or marketing.

The salary prospects for this position are also above average, and no slowdown in demand is expected in the near future.

Top Vacancies: Data Engineer Jobs (m/f/d)

Faq

In Switzerland, a Data Engineer earns an average of CHF 110’000.00 gross per year. The entry-level salary is also attractive, around CHF 90’000.00. The salary amount strongly depends on the level of experience, location, and industry of the company. Due to high demand, Data Engineer salaries are above average.

In Switzerland, a Data Engineer earns an average of CHF 110’000.00 gross per year. The entry-level salary is also attractive, around CHF 90’000.00. The salary amount strongly depends on the level of experience, location, and industry of the company. Due to high demand, Data Engineer salaries are above average.


For a career as a Data Engineer, it is advisable to obtain a master’s degree in a STEM field (mathematics, computer science, natural sciences, engineering) and acquire additional technical skills through continuous training (CAS). With this knowledge and skills, entering the world of data engineering should not be a problem.
For a career as a Data Engineer, it is advisable to obtain a master’s degree in a STEM field (mathematics, computer science, natural sciences, engineering) and acquire additional technical skills through continuous training (CAS). With this knowledge and skills, entering the world of data engineering should not be a problem.

The salaries of a Data Scientist and a Data Engineer are very similar in Switzerland: both roles earn an average of between CHF 100’000.00 and CHF 110’000.00.
The salaries of a Data Scientist and a Data Engineer are very similar in Switzerland: both roles earn an average of between CHF 100’000.00 and CHF 110’000.00.

Data Engineers handle the large amounts of data that come into a company through various means. Their task is to process this data using ETL (Extract, Transform, Load) tools and make it available for further analysis.
Data Engineers handle the large amounts of data that come into a company through various means. Their task is to process this data using ETL (Extract, Transform, Load) tools and make it available for further analysis.

There are no specialized studies to become a Data Engineer. Generally, Data Engineers are professionals in computer science or statistics who enter this field. It is recommended to obtain a master’s degree in a relevant field to work as a Data Engineer. Many CAS courses can further prepare you for this profession.
There are no specialized studies to become a Data Engineer. Generally, Data Engineers are professionals in computer science or statistics who enter this field. It is recommended to obtain a master’s degree in a relevant field to work as a Data Engineer. Many CAS courses can further prepare you for this profession.