What is a data engineer?
A data engineer is responsible for designing, building, and maintaining the data infrastructure. They are the central point of contact for the systems that enable the collection, storage, processing, and analysis of data. A data engineer works closely with data scientists, data analysts and other stakeholders. Working as a team, these officers ensure that data pipelines and data flows are efficient, scalable, and reliable.
Data engineers know Python, SQL and Scala like the back of their hand. But, these days, with the rise of big data and cloud computing, they must have more up their sleeves. A modern data engineer also has a vision of the latest technologies like Apache Hadoop and Spark, and cloud platforms like AWS or Azure, ChatGPT, and so on.
A data engineer adds value by ensuring that data is well-structured, cleansed, and available for analysis at the right time. Thanks to the data engineer’s pioneering work, organizations can now make data-driven decisions. At the same time, managers get valuable insights “in their lap,” significantly increasing the value of your organization’s “data assets.”
What are the duties of a data engineer?
The main duties of a data engineer are:
Development of data pipelines. Data engineers are responsible for designing and developing robust and efficient data pipelines. In doing so, they extract data from various sources and convert it into a suitable format. The data is then loaded and stored in storage systems or data warehouses. In this way, the data engineer ensures a smooth flow of data throughout the pipeline. The data engineer ensures data quality, data integration, and data cleansing.- Data infrastructure management. Data engineers build and maintain the infrastructure needed to store and process large amounts of (big) data. The tasks also include setting up and configuring databases, data lakes, and distributed computing systems. Data engineers optimize the procedures and mechanisms for storing and retrieving data and implement security measures. They also monitor system performance to ensure scalability and reliability.
- Data modeling. Data engineers work with data scientists and analysts to understand their data requirements and design appropriate data models. They define the structure, relationships, and constraints to organize data effectively. By implementing efficient data structures, they enable smooth query and analysis processes.
- Data governance and security. Data engineers play a crucial role in ensuring data governance and security. They take measures to protect sensitive data, establish access controls, and monitor data usage. In doing so, they adhere to privacy protection regulations and industry standards to maintain data integrity and confidentiality.
- Collaboration and communication. Data engineers work closely with various stakeholders, including data scientists, analysts, and business users. They work together to understand information needs, provide technical support, and translate business needs into technical solutions. Effective communication and teamwork are essential for the data engineer to align their work with the organization’s SMART goals.
Development of data pipelines. Data engineers are responsible for designing and developing robust and efficient data pipelines. In doing so, they extract data from various sources and convert it into a suitable format. The data is then loaded and stored in storage systems or data warehouses. In this way, the data engineer ensures a smooth flow of data throughout the pipeline. The data engineer ensures In summary, data engineers are responsible for creating and maintaining a robust data infrastructure. They enable efficient data processing and analysis. Thus, they enable organizations to effectively leverage their data assets.
What competencies and skills must a data engineer possess?
A data engineer has a number of specific competencies and skills that can contribute greatly to the success of the organization. Here are some of the most important ones:
- Understanding data architecture. A data engineer has a good understanding of the principles of data architecture. Designing and developing scalable and efficient data pipelines, data warehouses and data lakes is crucial here. The data engineer is able to apply their knowledge of data modeling, database design, and distributed systems as well.
- Programming skills. A good command of programming languages such as Python, Java, or Scala is essential for data engineering. Therefore, the data engineer must be able to write efficient and clean code. The data engineer is familiar with the frameworks and libraries commonly used within the so-called data engineering ecosystem, such as Apache Spark, Apache Kafka, or Apache Airflow.
- Experience with data integration and ETL. Extract, Transform, Load (ETL) is a fundamental part of data engineering. A data engineer must have a good understanding of data integration techniques and tools to extract data from various sources, transform it into a usable format, and load it into data storage systems.
- Data warehousing expertise. The data engineer is familiar with key data warehousing concepts and technologies such as SQL and relational databases. In particular, consider PostgreSQL or MySQL, such as Amazon Redshift or Google BigQuery. Understanding of data partitioning, indexing, and query optimization is important for efficient data retrieval.
- Knowledge of big data technology. Data engineers often work with large-scale data sets, so knowledge of big data technologies and platforms is important. Familiarity with frameworks like Apache Hadoop and Apache Spark, and distributed file systems like HDFS, is valuable for processing and analyzing big data.
- Knowledge and experience with Cloud Platforms. Cloud computing platforms, such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP), are widely used by data engineers. Understanding cloud services such as AWS S3, AWS Glue, Azure Data Factory, or GCP BigQuery is therefore essential for building scalable and cost-effective data solutions.
- Understanding data quality and governance. Ensuring data quality, integrity, and governance is a critical responsibility of data engineers. Understanding data validation techniques, data cleansing, and implementing data quality frameworks is therefore important to maintain data accuracy and consistency.
- Willingness to collaborate and communication skills. Data engineering requires working in multidisciplinary teams and collaborating with data scientists, analysts, and business stakeholders. Effective communication and collaboration skills are therefore needed to understand business requirements, translate them into technical solutions, and communicate complex concepts effectively.
- Problem solving skills. Data engineering projects can involve complex challenges and technical problems. Data engineers must be able to analyze problems, identify bottlenecks, and solve problems quickly and efficiently.
- Lifelong learning. The field of data engineering is constantly evolving with new technologies and best practices. Therefore, a mindset of continuous learning, staying abreast of industry trends, and being open to acquiring new skills and knowledge are essential to the long-term success of the data engineer.
Remember that this list is not exhaustive and is subject to change.
Related roles
Differences between data scientists and data engineers
The roles of a data scientist and a data engineer are different but complementary. Below we outline the main differences between the two roles based on three aspects: focus, skills, and tasks.
In short, data scientists focus on deriving insights and building models using data, while data engineers focus on designing and maintaining the infrastructure and systems that enable data analytics. Both roles work closely together, with data engineers providing the necessary data infrastructure and pipelines for data scientists to work with.
Hiring a data engineer
Hiring a data engineer at Passionned Group follows a set, proven procedure. After the assignment is issued, the intake begins with the creation of a profile and the definition of the requirements for the data engineer role. The necessary skills, experience, and qualifications are further defined. The consultants actively search for potential candidates through their existing network and by placing job postings on social media channels. Passionned Group reviews the resumes received and short-lists candidates who meet the key criteria. Appointments are made for introductory interviews with the client.
How does the data engineer fit into the overall picture?
If you too want to build a future-proof, data-driven organization, then the position of data engineer is indispensable. They are an important connecting link that ensures that data analysts and data scientists can rely on the data infrastructure. If you want to know how to properly align all these disciplines or have other questions about data engineering, you can contact one of our data consultants for objective advice.
About Passionned Group
Passionned Group is a specialist in interim data engineers. Our data consultants help organizations towards a data-driven future. When you hire a freelance data engineer through our platform, you can be sure that they are specialized in setting up a robust data architecture. Every other year we organize the Dutch BI & Data Science Award™. 
