Job profile:
Data Engineer (m/f/d)
Data is considered the "gold of the 21st century". In a world in which this data is generated in incredible quantities, it is necessary to organize and maintain this amount of data. For this often complex task, a so-called data engineer is needed in large companies.
Find out here what skills a data engineer needs to have, what salary can be expected and what the differences are to other big data jobs, such as data scientist.
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What is a data engineer? Definition and tasks
A data engineer ensures a functioning data infrastructure in a company, creates databases and maintains existing data records.
Data engineers are modern visionaries who use data to lead companies into a better future. They use their expertise and strategic thinking to simplify complex projects by organizing and processing the flood of data.
In the course of digitalization, the need for qualified IT specialists is constantly growing in data-driven organizations. They collect the data, process it and then evaluate it. The data engineer therefore provides (large) companies with what may be their most important asset: data.
Data engineers, also known as data architects, smart data architects, database engineers or data technicians, collect, prepare and check this data. Qualified data engineers are therefore indispensable for companies that want to meet the requirements of today's economy.
The opportunities on the job market look extremely good for these experts, as data engineers are needed wherever a lot of data is generated. In addition to companies in the field of information technology, organizations from a wide range of specialist areas such as healthcare, engineering, automotive, e-commerce, finance, banking or insurance are also looking for well-trained data engineers.
Data Engineer salary in Austria:
What you earn as a Data Engineer in Austria
he salary of a data engineer depends on qualifications and experience. As their career progresses, data engineers develop into senior data engineers, business intelligence architects and finally (big) data architects.
The demand for (Big) Data Engineers is continuously increasing on the job market. You can therefore expect a generous salary that is well above the average Austrian salary - depending on your qualifications and experience, of course. With increasing experience, you will develop into a Senior Data Engineer. Depending on your career level, company size and location, as a data engineer in Austria you will earn between €55,000 and €80,000 per year on average. Salaries are above average, especially in large Austrian cities.
The Hays IT Salary Report 2023 shows: 38% of employees in the data sector in Germany are satisfied with their earnings, 75% are willing to change jobs.
Of all IT professionals, specialists in the field of data and analytics earn the highest average annual salaries - however, it should always be noted that individual salaries depend on a variety of other factors.
Starting salary for data engineers in Austria:
What junior data engineers earn
The starting salary of a data engineer in Austria is also impressive. As a junior data engineer, you can expect a salary of between €40,000 and €50,000 per year. In smaller companies, the salary may also be lower. The sector is also decisive - earnings are higher in the technology or finance sector.
Most companies require a Master's degree for this job, which means that Bachelor's graduates can usually expect a lower starting salary.
Senior Data Engineer salary:
what experienced specialists earn in Austria
Big Data Engineer: Salary in Austria
You can also expect similar salary ranges in the Big Data sector. At the beginning, you can expect to earn around €52,000, which can increase by leaps and bounds after just a few years.
Data engineering tasks: What does a data engineer do?
Collecting, preparing and checking data - that is the job of data architects. Using technological possibilities, they transfer internal and external data sources into a data and analysis infrastructure. Data technicians use this for further analyses.
The processing of data - also known as handling - is one of the most important tasks of a data engineer. The most important process here is the so-called ETL: extract, transform, load.
- Maintain, prepare and save current data
- Prepare data for data scientists or data analysts
- Ensure the quality, reliability and performance of the data infrastructure
- Integrate data into a central, robust and flexible analysis infrastructure
- Model scalable database architectures (also cloud-based).
For these tasks, a data engineer uses a variety of different technologies and tools, such as:
- Big data technologies such as Hadoop, Apache Spark and other No-SQL databases (i.e. non-relational databases),
- Cloud technologies such as AWS (Amazon Web Services) or GCE (Google Compute Engine),
- relational databases or
- ETL (extract, transform, load) tools
Their work forms the basis for data science activities and enables the professional use of data. Using data pipelines - a series of data processing elements - they ensure an automatic flow of data. For example, the data ends up in a so-called data warehouse, a central repository where an organization's data from various sources is stored.
The difference to big data engineering
Data engineers are sometimes also referred to as cloud data engineers or big data engineers. The terms refer to the same activity with different nuances. Big data involves huge amounts of data. The distinction between the two role designations is therefore primarily about the amount of data. In many cases, they also differ in the tools they use for their work.
Data engineering vs. data scientist: a comparison
Whether engineer, scientist or analyst - all three deal with data. And yet there are differences. Data engineers are at the beginning of the process, where everything revolves around big data or data processing and creation. Data scientists and data analysts ultimately work with this data.
While the world of data engineers revolves around the standardization of data, data scientists master the interpretation of data chaos.
Data engineers are responsible for the development, maintenance and optimization of data infrastructure and pipelines and collect and process data. The data they collect is stored in various formats, databases or text files.
Data scientists are experts in data analysis. Using measures such as tracking or monitoring, they generate a structured database from raw data. With their business know-how, they create the basis for recommendations for action or decisions and thus answer important questions in their industry with the help of data. This is how big data becomes smart data.
Data scientists develop and improve methods for analyzing data that are used to collect data on company-relevant issues. They have experience in mathematics, especially in statistics and stochastics, machine learning and data visualization and are proficient in programming languages such as Phyton, Julia, R or SQL. In order to present the data obtained and the resulting recommendations for action to the relevant specialist departments, you will need strong communication skills.
Both roles work closely together: While a data engineer receives, stores and organizes the data, the data scientist gets ready to examine this data. In some cases, the data scientist hands over his/her results to the data analyst for in-depth analysis.
How do you become a data engineer?
Training, studies & further education
It is particularly important for prospective data engineers to understand ETL (extract, transform, load), i.e. the data cleansing process and the correct use of common tools such as Python.
Data Engineer training
There is (still) no standardized training for data engineers in Austria. The career path into the world of big data can therefore look very different and means a lateral entry for many. Although a degree in this field is welcome and is definitely an advantage when looking for a job, trained specialists in (business) informatics and computer technology or statistics are also among the attractive candidates for jobs in data engineering.
After graduating, you have a wide range of further training courses to choose from. Depending on the course, you can learn the basics of programming, big data, databases and automation.
Data Engineer studies
lthough data engineering is not a traditional field of study, some faculties now offer a degree course in it. At the FH Kufstein, for example, it is possible to complete a Master's degree in "ERP Systems and Business Process Management".
For studies of this type, an aptitude test is usually required to assess the skills and knowledge of the candidates.
Apart from that, there are numerous fields of study that make it easier to enter the world of big data: Business informatics, computer science, data management, computer engineering or statistics are among the classics. Students in these subject areas already learn a large part of the theory and skills required for a career in data engineering. These courses are offered at most major universities and many universities of applied sciences in Austria.
Data Engineering: further education and training opportunities
Due to the high demand for qualified specialists, there are a large number of further training courses for data engineers on the market. These include further training courses on topics such as cloud computing, programming languages, big data technology and automation. As a data engineer, you also have the right qualifications for further training or retraining as a data analyst.
Other helpful training courses will certify you in big data technologies such as Hadoop, Spark or Apache Kafka, which is very popular with companies.
Data engineering: lateral entry as a classic path
As there is no classic degree program for a career in this field, data engineers are usually typical career changers. After studying business informatics or statistics, various further training courses are a good way to embark on this career path.
Interested individuals can choose from a variety of different learning formats and course content and decide whether they want to start on a fixed date or prefer self-directed and flexible learning - the offers from providers in Austria such as the WIFI, AMS or ITLS are diverse and easy to find online.
It's not just graduates with a career change who currently have immense opportunities to pursue a promising career in this field. Even if you have a degree in statistics, the demand is there and you can continue to qualify as a data engineer according to the "learning on the job" principle. Various providers also offer further training in this area.
Data engineering skills:
You should have these skills
In order to secure the supply of data for companies, data engineers need to have a number of skills. Their soft skills are also required on an interpersonal level: they regularly interact with people from other departments and customers in their day-to-day work. They therefore need strong communication skills in order to solve problems in a team and ultimately lead projects to success.
They should also motivate other employees with their hands-on mentality and proactively look for solutions and optimizations when systems and data processes do not work as intended.
In summary, Data Engineers should have the following technical skills to perform their tasks efficiently and effectively:
- Technical understanding of big data infrastructures and technologies: This includes languages such as SQL
- Knowledge of software, programming languages and machine learning
- Excellent database skills
- Understanding the ELT process: A method that stands for Extract, Transform, Load and is used for large data pools and in the cloud area
- Certificates in Big Data technologies such as Hadoop, Spark or Apache Kafka are an advantage
Soft skills for Data Engineers include:
- analytical skills
- Logical and abstract thinking
- Very good communication skills
- strong ability to work in a team
- Time management and self-organization
Data Engineering: Opportunities on the job market
As the demand for data technicians has increased immensely in recent years, the opportunities on the job market for big data are very good.
These specialists are needed across all sectors in many companies that come into contact with Industry 4.0, IoT (Internet of Things) or the customer journey. Engineers from the mechanical engineering, automotive or chemical industries who have opted for big data are therefore particularly in demand.
In principle, talented data engineers are in demand in all large companies that have to cope with and manage a large amount of data. This can also be the case in e-commerce or marketing, for example.
Your salary prospects in this job are also above average and there is no sign of demand leveling off any time soon.
There is currently a high vacancy rate for vacant data engineer jobs in Vienna alone. In addition to companies from the information technology sector, companies from the healthcare, engineering and finance sectors are also looking for experts.
Top Vacancies: Data Engineer Jobs (m/f/d)
FAQ
On average, a data engineer in Austria earns between €55,000 and €80,000 gross per year. The starting salary, which is around €50,000, is also impressive. The level of salary depends heavily on the level of experience, location and industry of the company. Due to the high demand, data engineering salaries are above average.
On average, a data engineer in Austria earns between €55,000 and €80,000 gross per year. The starting salary, which is around €50,000, is also impressive. The level of salary depends heavily on the level of experience, location and industry of the company. Due to the high demand, data engineering salaries are above average.
For a career as a data engineer, it is advisable to complete a Master's degree in a STEM (science, technology, engineering and mathematics) program and to acquire additional technical skills through further training. 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 complete a Master's degree in a STEM (science, technology, engineering and mathematics) program and to acquire additional technical skills through further training. 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. The Data Scientist earns around €55,000 in Austria. On average, the data engineer earns slightly more than the data scientist.
Data engineers take care of the large amounts of data that end up in a company in different ways. Their task is to process this data with the help of ETL tools (extract, transform, load) and make it available for further analysis.
There is no specialized course of study to become a data engineer. As a rule, data engineers are IT or statistics specialists who are venturing into this career field. In general, a Master's degree in a relevant field is recommended to work as a data engineer.
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