Data Engineer: What They Do, Responsibilities, Salary, and What to Study

The growth of Big Data, artificial intelligence, and advanced analytics has increased the demand for professionals capable of managing the infrastructure required to work with large volumes of information. One of the key roles in this field is the Data Engineer, who is responsible for building and maintaining the systems that allow organisations to collect, transform, store, and use data.

Their work is essential for other specialists, such as Data Scientists and Data Analysts, to access reliable data that is ready for analysis. Building a career in this field requires a combination of programming, database, and cloud skills, together with knowledge of data architecture and management.

What Is a Data Engineer and Why Are They Considered the Architect of Big Data?

A Data Engineer is the professional responsible for designing, building, and maintaining the infrastructure needed to manage large volumes of information. Their goal is to ensure that data from different sources can be stored, processed, and made reliably available to other professionals and departments within the organisation.

A company may generate information through applications, websites, internal systems, devices, business operations, or external platforms. Without the right infrastructure, this data can be scattered across different systems and formats. The Data Engineer develops the processes needed to connect these sources and transform the information into datasets that are ready to use.

This is why Data Engineers are often considered the architects of Big Data. Their main role is not to interpret results, but to build the systems that ensure data reaches the people who need to analyse it in the right way.

Data Engineering is also part of a broader information management strategy. While professionals such as the Chief Data Officer may define how data should be used from a strategic perspective, the Data Engineer works on the technical infrastructure required to make that possible.

Main Responsibilities: Building the Data Infrastructure

The responsibilities of a Data Engineer cover a significant part of the data lifecycle. Their work begins when information is generated from different sources and continues until it is organised and ready to be used.

Their main responsibilities include:

• Collecting data from different systems and sources.
• Designing and maintaining data pipelines.
• Developing ETL and ELT processes to extract, load and transform data.
• Cleaning, structuring and standardising data.
• Designing databases and storage systems.
• Automating processes related to data flows.
• Ensuring data quality, availability and security.
• Monitoring data infrastructure performance.
• Optimising systems to process large volumes of information.
• Collaborating with Data Scientists, Data Analysts and other technology professionals.

One of the key concepts is the data pipeline, which can be understood as the journey data takes from its source to the system where it will ultimately be used. The Data Engineer designs and maintains this process to ensure that data flows correctly.

They also work with ETL processes (Extract, Transform, Load) and ELT (Extract, Load, Transform), which are used to extract data from different sources, transform it and integrate it into storage systems.

Their responsibility does not end once the data reaches its destination. They must also ensure that it is reliable, identify errors, monitor infrastructure performance and resolve any issues that may arise.

Key Differences: Data Engineer vs. Data Scientist vs. Data Analyst

Data Engineers, Data Scientists and Data Analysts all work with data, but they do so at different stages and with different objectives.

The Data Engineer builds the infrastructure and prepares the data. The Data Scientist uses that information to develop statistical, predictive or machine learning models. The Data Analyst, meanwhile, analyses data to interpret results, identify trends and produce reports that support business decision-making.

The Data Analyst, meanwhile, analyses data to interpret results, identify trends and produce reports that support business decision-making.

The three roles complement one another. Without the right infrastructure, Data Scientists and Data Analysts may have to spend a significant amount of time collecting, cleaning and preparing information before they can analyse it.

There are also roles that sit between these specialisations. A Machine Learning Engineer, for example, focuses particularly on implementing and running machine learning models in real-world environments.

The Data Analyst, meanwhile, uses different data analysis tools to turn available information into useful business insights.

Technical Skills and Tools You Need to Master (SQL, Python, Cloud) 

Working as a Data Engineer requires a solid technical foundation, particularly in programming, databases and systems architecture. However, you do not need to master every existing technology from the outset.

SQL is one of the core skills. It allows you to query, transform and manage information stored in databases and analytical systems.

It is also particularly important to understand how Data Engineers use Python, as it is widely used to develop transformation processes, automate tasks and work with large volumes of information.

As projects become more complex, other technologies also come into play:

• Databases and SQL: PostgreSQL, MySQL, SQL Server or equivalent systems.
• Programming: mainly Python, although Java or Scala may also be used.
• Big Data processing: Apache Spark and PySpark.
• Real-time processing: technologies such as Apache Kafka.
• Orchestration: tools such as Apache Airflow.
• Transformation: platforms such as dbt.
• Cloud: Amazon Web Services (AWS), Microsoft Azure or Google Cloud Platform (GCP).
• Storage: Data Warehouses, Data Lakes and Data Lakehouse architectures.

EAE Barcelona’s current Business Analytics & AI programme includes technologies such as Python, SQL, Databricks, Big Data architectures and Data Lakes, as well as specific content on Data Engineering.

These technical capabilities should be complemented by skills such as logical thinking, problem-solving, attention to detail and the ability to collaborate with different professionals.

Understanding business needs is also essential. A Data Engineer does not build infrastructure simply because it is technically possible: they need to understand who will use the data, what they need it for and what quality, speed or availability requirements must be met.

How Much Does a Data Engineer Earn in Spain? Salary and Demand

A Data Engineer’s salary depends on factors such as years of experience, level of specialisation, technologies mastered, company size and level of responsibility.

As a general reference, salary ranges may progress as follows:

These figures should be understood as approximate ranges, as actual salaries can vary considerably between companies and roles. Highly specialised senior professionals, architecture positions and certain international environments may offer salaries above these ranges.

Technical specialisation can make a significant difference. Expertise in cloud environments such as AWS, Azure or GCP, combined with experience in distributed processing technologies, automation and modern data architectures, can broaden career opportunities.

In addition, the growing use of artificial intelligence is increasing the importance of infrastructures capable of providing reliable information. AI and machine learning models require large volumes of data that have been properly collected, prepared and stored, placing Data Engineers in an important position within technology transformation projects.

What to Study to Become a Data Engineer: Take the Next Step with a Master’s Degree

Professionals who move into Data Engineering often come from STEM backgrounds such as Computer Engineering, Telecommunications Engineering, Software Engineering, Mathematics or related disciplines.

Backend developers, database administrators and Business Intelligence professionals can also transition into this field by expanding their expertise in Big Data, cloud technologies and data architectures.

Further specialisation makes it possible to develop a deeper understanding of the technologies and methodologies required to work with large volumes of information.

EAE Barcelona’s Master’s in Business Analytics & AI includes Data Engineering, Data Wrangling, Big Data architectures, Python, SQL, Data Governance and technologies such as Databricks. Data Engineer is also listed among the programme’s potential career opportunities.

For professionals who want to move towards a more strategic perspective, the Global Master’s in Business Analytics and Data Strategy covers the entire data lifecycle, from Data Governance, architecture and Data Engineering to Big Data, Machine Learning and Business Analytics, combining technology with business strategy.

Those who want to focus more specifically on artificial intelligence can choose the Master’s in Artificial Intelligence & Machine Learning for Business or the Master’s in AI Applied to Business, depending on whether they want to deepen their expertise in AI and Machine Learning or focus on applying these technologies to business processes and decision-making. Specialisation can open up several career paths:

• Data Engineer: building and maintaining data infrastructure and pipelines.
• Cloud Data Engineer: specialising in cloud platforms and architectures.
• Data Architect: designing an organisation’s overall data architecture.
• Analytics Engineer: preparing and modelling data for analytical use.
• MLOps Engineer: deploying and maintaining Machine Learning systems.
• Data Governance Specialist: overseeing data quality, availability, security and governance.

With experience and a stronger strategic focus, this career path can also lead to positions of greater responsibility in architecture, data strategy or management, eventually progressing towards roles such as Chief Data Officer.

Becoming a Data Engineer therefore requires a combination of a strong technical foundation and specialised knowledge in data, programming and cloud technologies. From there, further training allows professionals to choose whether to deepen their expertise in engineering, move closer to artificial intelligence or progress towards the strategic management of data.