Hiring an interim data engineer: tasks, competencies and skills

Business Partner
Author: Thomas Abramse MSc
Business Partner
Table of Contents

A data engineer is an indispensable link in intelligent, data-driven organizations. They are the architect of the data infrastructure. Data engineers design and build databases, data pipelines and data warehouses. They ensure that data is processed quickly, efficiently and securely. The data engineer is the builder and guardian of the foundations on which organizations make data-driven decisions. They make the data usable for data analysts and data scientists. By using technology and programming languages, the data engineer ensures that all systems at the operational level run smoothly. In this article, we take a closer look at the duties and competencies of a data engineer. What makes someone a good data engineer? In which processes can a data engineer add value? And what is actually the difference between a data engineer and a data scientist?

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 with the rise of big data and cloud computing, they must have more up their sleeves these days. A modern data engineer also has a vision of the latest technologies like Apache Hadoop, 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:

  1. Data engineeringDevelopment 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.

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.

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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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.

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.

What does a Data Engineer do?

  • Focus. Data engineers are responsible for building and maintaining the infrastructure and systems needed to enable data analysis. Their focus is on designing, developing and optimizing data pipelines, databases and data warehouses to ensure efficient data processing and storage.
  • Skills. Data engineers have strong programming and database skills. They are proficient in languages such as Python, SQL and technologies such as Hadoop, Spark and cloud platforms. They have expertise in data integration, data cleansing and data pipeline development.
  • Duties. Data engineers handle so-called data ingestion (importing large amounts of data), transformation and storage processes. They build data pipelines to pull data from various sources, transform it into a usable format and load it into databases or data warehouses. They optimize the data infrastructure for performance, scalability and reliability.

What does a Data Scientist do?

  • Focus. Data scientists work primarily with data to gain insights, build predictive models and make data-driven decisions. Their main goal is to discover patterns, trends and relationships in data to solve complex business problems.
  • Skills. Data scientists have strong skills in statistical analysis, machine learning and data modeling. They are proficient in programming languages such as Python or R and use tools such as TensorFlow or scikit-learn for analysis and modeling tasks. They understand statistical concepts and algorithms.
  • Tasks. Data scientists explore and visualize data, perform advanced analysis, develop and train machine learning models, and evaluate model performance. They often work with unstructured and “messy” data. Data scientists are able to experiment, do algorithm development and statistical inference, or generalizing observations, characteristics, properties from samples to the entire population. Inferential skills are skills that allow you to make inferences, in other words whether you can “read between the lines.”

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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 defining 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

Logo Passionned Group, the expert in data engineeringPassionned Group is the 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™.

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