What Does a Data Engineer Do?

In a previous article, we wrote about non-technical roles in the IT industry. Now, it’s time to make a 180-degree shift and explore the profile of one of the most technically advanced positions, but also one of the most sought-after in this field: Data Engineer. We will take a detailed look at what it means to be a Data Engineer, from day-to-day tasks to career prospects in the field of artificial intelligence.

In today’s technological landscape, dominated by AI and Big Data, the role of Data Engineer has become one of the highest-paid on the job market everywhere. Companies in all industries rely on data to make strategic decisions, and data engineers are the architects behind the scenes who make this possible.

What Does A Data Engineer’s Role Consist Of?

At a macro level, a Data Engineer is responsible for designing, building, and maintaining the infrastructure that enables the collection, storage, and processing of massive volumes of data. 

Think of them as the civil engineers of the digital world; they build the “pipelines” through which raw data is transformed into valuable information. Their mission is to ensure that data is available, reliable, and accessible to data analysts, data scientists, and other decision-makers within an organization. 

Without a solid foundation built by data engineers, the entire ecosystem of data analytics and artificial intelligence would function inefficiently.

The Core Professional Tasks of a Data Engineer

The day-to-day activities of a Data Engineer are varied and involve a combination of programming, database management, and systems architecture. Their fundamental tasks include:

ETL/ELT

Designs and implements ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes to move data from various sources (databases, APIs, files) to a centralized repository (data warehouse or data lake).

Data modeling

Creates and optimizes database schemas to ensure efficient data storage and querying.

Building “Data Pipelines”

Develops automated and scalable data flows that reliably transport and process data.

System monitoring

Constantly monitors data systems to identify and resolve performance or data integrity issues.

Data architecture design

Defines the overall data management strategy at the company level, including the selection of appropriate technologies.

Data quality assurance

Implements processes and tools to validate and maintain the accuracy of company data.

Performance optimization

Continuously analyzes and improves the performance of data pipelines and databases.

Data security

Implements security measures to protect sensitive data in accordance with applicable regulations (e.g., GDPR).

Collaboration with organizational management

Understanding business needs and translating them into technical requirements for data systems is also part of a Data Engineer’s job.

Specific Skills

To excel in this role, a Data Engineer must have a solid set of technical and non-technical skills:

Technical skills

Programming languages

Solid knowledge of Python and SQL is essential. Scala and Java are also frequently used.

Database technologies

Experience with relational databases (e.g., PostgreSQL, MySQL) and NoSQL (e.g., MongoDB, Cassandra) is a must.

Big Data frameworks

Knowledge of the Hadoop ecosystem (HDFS, MapReduce) and, in particular, Apache Spark is crucial for large-scale data processing.

Cloud services

Experience with cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure and their specific data services (e.g., AWS S3, Redshift, GCP BigQuery, Azure Data Factory) is required.

Containers and orchestration

Familiarity with Docker and Kubernetes is becoming increasingly important.

Non-technical skills

Analytical thinking and problem solving

A Data Engineer must have the ability to understand complex systems and find effective solutions to technical challenges.

Communication and collaboration

Frequent interaction with other teams (Data Science, Business Intelligence) requires excellent communication skills.

Attention to detail

Ensuring data quality and integrity is a key responsibility for a Data Engineer.

Training and Certification

Although there is no single path to a career in Data Engineering, most professionals in this field have a degree in computer science, software engineering, mathematics, or a similar field. Practical experience in software development or database administration is a considerable advantage.

In addition to formal education, industry-recognized certifications can validate the skills of an emerging Data Engineer and increase their chances of employment. Among the most popular such certifications are:

• Google Professional Data Engineer
• Microsoft Certified: Azure Data Engineer Associate
• AWS Certified Data Analytics – Specialty
• Databricks Certified Data Engineer Associate/Professional

Salaries for a Data Engineer

Due to high demand and the complexity of the role, salaries for Data Engineers in Romania are very competitive. 

A junior Data Engineer can start with a net monthly salary of approximately RON 7,000, according to DevJob. 

With experience, a Data Engineer with an average level of experience can earn between RON 9,000 and RON 15,000. 

Senior Data Engineers, with extensive expertise and knowledge of systems architecture, can exceed the threshold of 20,000 RON net per month, depending on the company, project, and technologies used.

Data Engineer: A Bridge to the World of AI Programming

The role of Data Engineer is an ideal launch pad for a career in artificial intelligence (AI) and machine learning (ML). Data engineers build the foundation on which any AI project is based. They are responsible for providing clean, structured, and large volumes of data, which are essential for training machine learning models.

A Data Engineer who wants to transition to AI can naturally evolve into an ML Engineer role. Data processing skills, knowledge of Python and Spark, and experience in building scalable pipelines are directly transferable. By adding knowledge of machine learning algorithms and frameworks such as TensorFlow or PyTorch, a data engineer is perfectly positioned to build and implement artificial intelligence solutions in production. Thus, a career as a data engineer is not only profitable today, but also a strategic investment in the future of technology.