what does a data engineer do?
According to the definition by MIT Sloan School of Management, a data engineer is responsible for collecting, managing, and administering data. They focus on the critical part of data processing, as they create the architecture for acquiring and processing raw data. They also prepare the data for data scientists to analyze and extract insights. As part of their preparatory work, data engineers identify trends within datasets and develop algorithms. Like many IT roles, data engineers possess deep and specialized technical knowledge in areas such as database design, various programming languages, and cloud services.
Data engineers are part of a team that provides critical information to business leaders to support their daily operations and long-term strategic goals. These analytics teams play a vital role within the organization, as it’s essential for leaders to quickly understand and respond to current and emerging trends.
what is it like to work as a data engineer?
A data engineer works closely with other IT colleagues to develop architecture and create interfaces (APIs) that improve data usability. Whether preparing information for use on a dashboard, importing it into a database, or extracting it for other purposes, the data engineer is responsible for ensuring the integrity of the data and processes. Their tasks also include combining different datasets, determining how information is stored, and collaborating with data scientists and analysts to extract the necessary insights.
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average salary of data engineers.
Our salary calculator allows you to explore pay ranges and see how much a beginner or experienced data engineer earns.
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salary calculatorhow can you earn more as a data engineer?
The following options can all contribute to a higher salary:
- Gain relevant experience: you can acquire practical skills by working on real data projects, whether through internships, freelance opportunities, or personal projects. This hands-on experience showcases your abilities and expertise to potential employers.
- Continuously update your skills: data engineering is a rapidly evolving field. Stay current with the latest technologies and trends, such as cloud computing, big data frameworks, and machine learning. Participate in relevant training programs, workshops, or online courses to expand your knowledge.
- Specialize in a specific segment: gaining experience in a particular field or industry can make you a sought-after professional. For example, specializing in healthcare data management or financial data management can open up lucrative career opportunities.
- Obtain the appropriate certifications: certificates from reputable organizations, such as AWS Certified Big Data – Specialty or Google Cloud Certified – Data Engineer, validate your skills and enhance your marketability.
- Networking: attend industry events, join data engineering communities, and connect with professionals in the field. Building your network can open up new job opportunities and provide valuable insights.
types of data engineer roles.
Data engineers typically work in three areas.
- Generalist: within an organization, they oversee all data-related tasks, including analytics. They design programs and algorithms, manage and analyze data, and create reports based on the results for the relevant teams within the company.
- Pipeline-oriented: the process-focused data engineer typically works closely with data scientists to transform data into meaningful information. This role is usually more complex than a generalist position, especially in mid-sized companies, because it requires an understanding of the organization’s data systems and objectives. They monitor data integration tools that connect data warehouses to data sources.
- Database-centric: it creates databases. Companies with extensive data typically require monitoring of their data repositories and analytical databases for schema development. It extracts data from the source and transforms it into a format that the company can analyze and store in its data warehouses.
The size of an organization often determines the role of a data engineer, as smaller companies may have just a small team or even a single generalist handling data management. Larger organizations with more resources can employ multiple data engineers to meet the demands of bigger data volumes and more extensive analysis needs.
working as a data engineer.
The responsibility of a data engineer is to ensure that the data provided to data scientists and other stakeholders is accurate and usable. To achieve this, they work closely with other team members, including application developers, data scientists, and database administrators.
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responsibilities of a data engineer
The responsibilities of a data engineer are based on the following:
- Data analysis and synthesis. you know how to create data profiles and perform source system analysis, and you can provide clear insights to your colleagues to support the end-use of data.
- Data development process. capable of designing, building, and testing data products based on feeds from multiple systems, utilizing various storage technologies and/or access methods. Understands how to create repeatable and reusable products.
- Adatinnováció. is familiar with new tools and the innovative opportunities related to data utilization.
- Data integration planning. develops data solutions in accordance with approved organizational standards, ensuring flexibility, customization, and future-proofing of services.
- Data modeling. understands the concepts and principles of data modeling and is capable of creating, maintaining, and updating appropriate data models to meet specific business needs. Knows how to reverse-engineer data models from an active system.
- Metadata management. With the help of metadata repositories, you can perform complex tasks such as data and system integration impact analysis. You know how to maintain a data warehouse to ensure that information remains accurate and up-to-date.
- Problem solving (Data). familiar with the types of issues and solutions related to databases, data workflows, data products, and data services.
- Programming and structure. you can design, code, test, debug, and document simple programs or scripts under the guidance of others.
- Testing. You can run the test scripts under supervision. You are familiar with the testing process and how it works.
Based on this, the following tasks are performed daily:
- data extraction and preparation as part of the ETL (extract, transform, and load) processes
- designing and maintaining databases and data streams
- development and optimization of data architectures both locally and in the cloud
- optimization and automation of data processing processes
- ensuring data quality and performance through regular checks and optimization
- ensuring data security and data privacy.
- documenting data processes and preparing reports.
- collaborating with data scientists and analysts to design effective data models.
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work environment
Data engineers typically work within a team, either on-site or remotely. The tools and datasets they use are all digital, so there are no restrictions on where they can work from, as long as they have secure access to their servers. Ultimately, whether the work is done in person or virtually depends on the company's culture and policies.
Large organizations, such as financial institutions, healthcare providers, and technology companies, typically manage their data infrastructure with dedicated data engineering teams. Data engineers often work for consulting firms and assist clients in designing and implementing data solutions tailored to their specific needs.
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who does a data engineer work with?
As a data engineer, you'll work with a variety of professionals. Data engineers often collaborate in teams with other data-related specialists, such as data scientists, data analysts, and database administrators. Additionally, close cooperation with other business units and departments like marketing, business development, or IT is essential.
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working hours schedule
Data engineers' work schedules generally follow standard office hours. However, depending on project deadlines and specific organizational needs, some flexibility may be required regarding shifts and overtime. Since data infrastructure is critical to business operations, there may be occasions when data engineers need to be available for on-call support or troubleshooting, especially in cases involving real-time data processing or critical system issues.
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development opportunities
The popularity of the data engineer profession is steadily increasing due to the growing demand for digital transformation and big data solutions. There is high demand for data engineers across companies, especially as organizations recognize the importance of data-driven decision-making. Professionals working as data engineers have a wide range of opportunities in the job market and often find challenging, well-paying positions.
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why it's worth looking for a data engineer position through randstad
There are several advantages to working with a Randstad consultant when searching for a data engineer position:
- an advisor well-versed in the IT sector of the labor market supports the selection process
- you can find a wide range of job opportunities on our website
- whether you're looking for a fixed-term or permanent position, we help you find the job that best fits you
required studies and skills.
studies.
In order to work as a data engineer, acquiring the necessary professional knowledge is essential. Key knowledge areas include programming, mathematics, software development, data mining, database management, IT, and cybersecurity. Having solid technical expertise is expected of every data engineer, whether they are a generalist, pipeline specialist, or database-focused expert. It’s advisable to choose one of the following training programs to get started:
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university degree
Bachelor's, master's, or Ph.D. in the following fields: mathematics, information technology, computer science, engineering informatics, or software development.
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technical knowledge
In addition to a university degree, employers may also require certifications in one of several key technology areas. According to the CIO, the most in-demand certifications for data engineers are as follows:
- Amazon Web Services (AWS) Certified Data Analytics – Specialty
- Cloudera Certified Associate (CCA) Spark and Hadoop Developer
- Cloudera Certified Professional (CCP): Data Engineer
- Data Science Council of America (DASCA) Associate Big Data Engineer
- Data Science Council of America (DASCA) Senior Big Data Engineer
- Google Professional Data Engineer
- IBM Certified Data Architect – Big Data
- IBM Certified Data Engineer – Big Data
- SAS Certified Big Data Professional
skills.
A data engineer must possess advanced knowledge of data architectures, as well as database design and maintenance. To perform their job competently, a thorough understanding of various technologies and programming languages is essential - sometimes as many as 10 to 30 different tools may be needed to select the best options for the projects they are working on. Many organizations rely on cloud service packages from a single provider, so in-depth familiarity with platforms like AWS or Azure is often required.
The following skills are required for a data engineer to perform their job:
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technical skills
Apache Spark, SQL, Hadoop, Beam, Java, Python, R, Kafka, Extract/Transform/Load (ETL), Amazon Web Services, Databases, Shell scripting, distributed machine learning platforms: MLib (Spark), deep machine learning (TensorFlow, GPU Programming), development in containers (Docker, Rkt), programming in notebooks (Zeppelin, Jupyter), Java, C++, and/or Go, as well as functional languages (Scala, Clojure, Elixir).
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personal skills
In addition to technical expertise, many personal skills are essential, typically possessed by leaders. These include strong communication abilities, a team-oriented mindset, collaboration skills, effective project management, and efficient time management.
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coaching skills
An effective DevOps engineer always looks for opportunities to mentor and develop the skills of the team. They identify gaps in employee skills and provide training opportunities to develop the necessary skills.
frequently asked questions.
We have compiled the most frequently asked questions regarding the data engineer role:
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do data engineers perform similar work to data scientists?
Not entirely. Engineers focus on ensuring that the information underlying business analysis is accurate, clear, and ready for use by data scientists. These two roles often work closely together to ensure that the analysis produces insights that business leaders can understand and leverage to achieve their goals.
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can I get a job as a data engineer immediately after graduating from college?
Many employers are looking for candidates with at least a few years of professional experience in this field. However, due to a current shortage of data engineers, they are also seeking recent graduates with strong programming and technological skills, as well as good problem-solving abilities. The best way to quickly secure a position as a data engineer is to acquire the necessary technical knowledge during university, then enhance your profile by obtaining additional certifications and gaining experience through working on data projects.
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aren't data engineers just a subset of computer programmers?
Coding is a fundamental skill that data engineers must have, but their work is much more complex than just programming. They need to understand data architecture, databases, and distributed systems. They should be able to identify issues with data sets, develop solutions to address those problems, and integrate data into systems used for data analysis.
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is a university degree required to advance in this field?
Not all companies require their data engineers and data scientists to hold a master's degree, but it is highly recommended for those aiming for leadership roles. Many data professionals work in this field without a degree, leveraging their work experience and technical expertise to advance their careers. However, earning a master's or Ph.D. can provide a deeper understanding of theoretical concepts, which can enhance problem-solving effectiveness. Additionally, various certifications can also support career progression in this area.
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how can I apply for a data engineer position?
Applying for a data engineer position is easy: create your Randstad profile and browse our job offers near you. Once logged in, you can apply with just one click for the opportunity that interests you on our website!
