What is a Data Engineer?

Collecting data is good. Being able to store it, make it reliable, and analyze it is better! To do this, companies need experts combining technical skills with an understanding of business issues: Data Engineers. Here's a summary of everything you need to know about them.

Job profile updated 04/13/2026.

What is data?

All companies collect data, intentionally or not. Through their commercial, marketing, and operational activity, they capture every day a volume of data proportional to the intensity of their activity: CRM, connected objects, social networks, search engines, user-driven service usage, etc.

Over the past two decades or so, with the exponential growth of technology, they have been learning to leverage this data — internally to improve their product/service, support decision-making, automate through AI, measure, etc., or externally by reselling it to other companies.

Now that everyone has understood the importance of data, the goal is no longer to lose a single drop of it. It's the new wealth of the 21st century: as oil reserves are running dry, data reserves are filling up… and they have no limit.

What is the role of the Data Engineer?

More and more companies are hiring data engineers to understand and visualize data.

Why do companies need this role?

Every company captures, through its activity, a significant amount of data and wants to leverage it in different ways:

• External: B2B resale
• Internal: improving the product, supporting decisions, measuring decision impact, etc.

We can distinguish between companies whose product and/or business depends on data collection (advertising, digital marketing, social networks, streaming platforms…) and those that, by virtue of their activity, handle a large volume of data (media, marketplace platforms).

All these companies need a Data Engineer's skills at the entrance of the data value chain, to collect raw data, transform it into usable data, and then format it to make it available to:

• Data scientists, who need data in the right format to be consumed by algorithms.
• Data analysts, who use data through dashboards, visualization tools, or reporting.

Working upstream of data leverage, their impact is indirect on the business and happens through the work of Data Scientists and Data Analysts. It can be more or less significant depending on the volume of data the company collects.

But as the gateway to data, they are indirectly responsible for all the value that will be extracted from the data collected. Without this profile, it is very difficult, if not impossible, to imagine all the data-related tasks across the company.

The Data Engineer's responsibilities

A Data Engineer is someone with a technical background (most often in software development). They build the architecture of the Big Data system and must ensure that data can be collected, transformed, and stored from various sources. To do this, they develop solutions that can process a large volume of data within a limited time.

The Data Engineer's job is to lay the groundwork so that a Data Scientist can use "clean" data to leverage it in more complex ways, surface trends (insights), predict, and infer with Machine Learning algorithms.

The Data Engineer builds the architecture of the Big Data system. They will choose storage tools suited to the type of data and the storage/query ratio.

With an interest in Development and Operations (DevOps), they collaborate directly with the other data roles. They know how to balance production-readiness with the rapid iterations of development.

The main challenges they face are: performance, scalability, and managing large volumes of data.

Their role depending on company size

In a small tech team, they may carry the responsibility of an entire data function, combining the Data Engineer and Data Analyst roles. They will own the entire data value chain without being able to go deep into any single topic.

This requires horizontal knowledge of data issues without the ability to develop vertical expertise.

In a large tech team, they work under the responsibility of a Data Manager (Head of Data, CDO, Lead Data Manager), in collaboration with the Data Scientist and Data Analyst, who may work on the same decision-making problems but with different outputs.

The Data Analyst will develop visual tools (dashboards) and reporting, while the Data Scientist will build predictive models.

Responsible for setting up the architecture of the Big Data system (hence the alternative title data architect), they also work with DevOps teams to build the data reservoirs called Data Warehouses.

What kinds of problems does the Data Engineer tackle?

They will enable:

• Developing and setting up a process to collect, store, and model data. This is where they work on data infrastructure issues.
• Setting up relational and non-relational databases to enable access for Data Scientists and Data Analysts.
• Protecting and securing access to company data, to prevent other companies or users from accessing the database.
• Establishing a data policy compliant with GDPR, to always meet European user data protection standards.
• Putting their Big Data expertise to work in collaboration with other data team members (Data Scientists & Data Analysts).

Team collaboration

The Data Engineer will work with a Machine Learning Engineer, a Data Scientist, or a DevOps engineer.

What skills does a Data Engineer have?

They mostly use NoSQL databases and rely on the cloud for infrastructure. They also know how to use technologies like Airflow and Spark to properly orchestrate and process these large volumes of data.

In general, the Data Engineer has a developer background. To deliver the best solutions, they are an application developer with an appetite for IT infrastructure administration.

In short, the Data Engineer is a tech profile specialized in building software solutions around big data.

Their soft skills

Rigor, curiosity, communication, and team spirit are the key elements to being a strong Data Engineer.

Technologies & platforms used

The data engineer will work with several technologies, platforms, and tools:

• DB language: SQL, NoSQL
• Storage and ETL: Redshift, Teradata, Cassandra
• Processing and manipulation: Spark, Hadoop, Kafka
• Data analysis (Hadoop suite): Hbase, Hive
• Cloud skills: Microsoft Azure, AWS, GCP.

They will use programming languages such as:

• Python
• Java
• Go

and a specialized language such as (++):

• Scala
• Julia
• Perl.

What training is needed to become a Data Engineer?

The majority of Data Engineers have a background in an engineering school specialized in computer science or a Big Data Master's at university. Some Data Engineers are also former Software Engineers or Big Data engineers.

For those wishing to retrain in data engineering without going through a long curriculum, Jedha teaches the role through a data engineer training program of 150 hours focused on the Modern Data Stack (dbt, Airflow, Spark) and data pipelines for AI.

What is a data engineer's salary?

A data engineer's salary can double between a junior and a senior profile:

• Junior Data Engineer: €40K to €50K
• Confirmed Data Engineer: €48K to €70K
• Senior Data Engineer: €65K to €100K+

How can a data engineer's career progress?

Depending on the candidate's skills and soft skills, the career can progress to:

• Lead Data Engineer
• Head of Data
• ML Engineer
• Data Scientist

Are you a technical profile looking for new career opportunities? Don't miss our latest job openings.

Looking to hire a Data Engineer for your company? We can help. Bluecoders specializes in recruiting tech profiles. Get in touch.

FAQ about Data Engineers

What is a Data Engineer?

A Data Engineer is a technical profile responsible for collecting, transforming, storing, and making the company's data usable. Their role is to build the data architecture and prepare reliable data for the downstream data teams.

What does a Data Engineer do for a company?

They make data usable. Concretely, they collect raw data, transform it, and make it available in a usable format for Data Analysts and Data Scientists.

What does a Data Engineer do day to day?

Day to day, they design data pipelines, set up collection and storage systems, choose the right data architecture tools, optimize performance, and ensure that data is accessible, reliable, and secure.

What's the difference between a Data Engineer, a Data Analyst, and a Data Scientist?

The Data Engineer works upstream: they prepare the infrastructure and the data. The Data Analyst then exploits this data in dashboards, reports, and visualization tools. The Data Scientist uses it for advanced analyses and predictive models.

Why do companies need a Data Engineer?

As soon as a company collects a lot of data, it needs a profile capable of structuring it and making it reliable. Without this role, it becomes very difficult to leverage data to manage activity, improve a product, or develop advanced use cases.

In which companies is the Data Engineer role most useful?

The role is particularly useful in companies that depend heavily on data or that handle large volumes of it, such as platforms, media, players in digital marketing, advertising, or streaming.

What are the main responsibilities of a Data Engineer?

Their responsibilities mainly cover collecting, storing, transforming, and modeling data. They also set up appropriate databases, secure access, and contribute to a compliant data policy, particularly on GDPR topics.

What are the technical challenges of a Data Engineer?

The three big challenges are performance, scalability, and managing large volumes of data. The role therefore requires a real ability to design robust, fast, and scalable systems.

Does a Data Engineer also do data analysis?

Not as a main part of the role. Their core job remains data engineering. In small teams, however, they can cover a broader scope and partially take on responsibilities close to those of a Data Analyst.

Does the Data Engineer's role change depending on company size?

Yes. In a small team, they can cover almost the entire data value chain. In a larger structure, their role is often more specialized, with closer collaboration with a data manager, Data Scientists, Data Analysts, and DevOps.

Which teams does a Data Engineer work with?

They mainly work with Data Analysts, Data Scientists, ML Engineers, and DevOps. The role is very cross-functional, since it sits between business needs, analytical use cases, and infrastructure constraints.

What skills do you need to become a Data Engineer?

We recommend mastering databases, data architectures, distributed processing, and cloud environments. The role also requires a solid foundation in software development, since many Data Engineers come from a developer background.

What languages and tools does a Data Engineer use?

The technologies cited include SQL, NoSQL, Spark, Hadoop, Kafka, Airflow, as well as cloud solutions such as AWS, Azure, and GCP. On the language side, Python, Java, and Go come up often, sometimes with Scala, Julia, or Perl depending on the environment.

What soft skills are useful for a Data Engineer?

The most useful qualities are rigor, curiosity, communication, and team spirit. These are important points, since the role requires both technical precision and a real ability to collaborate with other data and tech profiles.

What training is needed to become a Data Engineer?

The most common path is through an engineering school in computer science or a Master's specialized in Big Data. Some bootcamps can also be considered. The role is also accessible to profiles already coming from software development or Big Data engineering.

Can you become a Data Engineer after a developer role?

Yes — it's even a frequent transition. A Software Engineer with an appetite for infrastructure, databases, and Big Data issues can move into this role quite naturally.

What is a Data Engineer's salary?

The ranges given are €40K to €50K for a junior profile, €48K to €70K for a confirmed profile, and €65K to over €100K for a senior profile. Salary then depends on the level of experience, technical stack, and type of company.

What career progression after a Data Engineer role?

A Data Engineer can move into roles like Lead Data Engineer, Head of Data, ML Engineer, or Data Scientist. So opportunities open up toward expertise, management, or more specialized roles in advanced data.

Is Data Engineer a future-proof job?

Yes, because companies continue to accumulate more and more data and need profiles capable of structuring it properly. The more strategic data becomes, the more value the Data Engineer role brings to the organization.

Data Engineer or Data Scientist: which to choose?

The right choice depends mostly on what you enjoy doing. If you prefer building architectures, manipulating data flows, and making data reliable, the Data Engineer role is a better fit. If you prefer models, prediction, and advanced analysis, Data Scientist will generally be more relevant. This distinction follows directly from the separation of roles described in the job profile.