Data & AI
Data Engineer: Salary and Responsibilities in 2026
Here is the data engineer job profile. What is their role? Their responsibilities? Their salary? What training is needed? What career progression is possible?
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Job profile last updated on .
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.
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 works with several families of technologies:
- Query languages: SQL, NoSQL
- Orchestration and transformation: Airflow, Dagster, dbt
- Distributed processing and streaming: Spark, Kafka, Flink
- Warehouses and lakehouses: Snowflake, BigQuery, Redshift, Databricks
- Hadoop ecosystem (Hive, HBase): still present in some legacy environments
- Cloud: AWS, Azure, GCP
On the programming language side, Python and SQL dominate, with Java, Scala, or Go depending on the environment.
What skills should you look for in a data engineer before hiring them?
From a hiring perspective, the right question isn't "do they know Spark?" but "can they reliably run, at a controlled cost, the pipelines my team needs?" A data engineer's resume often lists some twenty technologies. Before hiring them, here are the 7 skills to check, and how to test them in an interview.
| Skill | Why it matters | How to check it in an interview |
|---|---|---|
| Advanced SQL and data modeling | A bad model costs you for years in slow queries and conflicting numbers | Have them model a simple case (orders, customers, products) and justify the choices: star schema, historization |
| Orchestration and transformation (Airflow, Dagster, dbt) | This is the daily job: scheduled, tested, replayable pipelines | Ask how they handle a job that fails at 3am: retries, idempotence, alerting |
| Batch or streaming (Spark, Kafka, Flink) | Two different jobs under the same title | Have them describe a real-time pipeline they operated, with its latency constraints |
| Cloud data warehouse or lakehouse (Snowflake, BigQuery, Redshift, Databricks) | The architecture choice and the cloud bill depend on it | Ask how they've already reduced a querying or storage cost |
| Data quality and observability (dbt tests, Great Expectations, data contracts) | Wrong data costs more than missing data | Ask how they detect that an upstream source has changed format |
| Software engineering fundamentals (Python, Git, CI/CD, tests, Terraform) | A pipeline is code in production, with the same requirements | Have them review a pipeline code snippet and comment on what they'd fix |
| Governance and security (GDPR, access management, lineage) | Legal obligation and a condition for business teams' trust | Ask how they handle a personal data deletion request |
Which skills should you prioritize based on your context?
In a startup, the first data engineer is often on their own: prioritize versatility (ingestion, modeling, first dashboards) and the ability to prioritize business requests. In a scale-up or large company, look instead for a clear specialty: data platform, streaming, or analytics engineering. If your teams are building AI products, add one criterion: knowing how to prepare data usable by models (training sets, documents for a RAG system), working closely with your AI Engineers and MLOps Engineers.
On the soft skills side, one criterion sets strong profiles apart: knowing how to say "not now" to ad hoc requests that destabilize the platform, while staying at the service of Data Analysts and Data Scientists.
What Bluecoders sees on this profile
Our recruiters distinguish a batch-oriented data engineer from a streaming data engineer right from the brief: two profiles under the same title, but with different talent pools. When this is settled early, timelines follow. According to our 2026 data, data profiles are regularly hired in under 3 weeks, like a Data Engineer at Lucky Cart in 3 weeks or a Lead Data Engineer at Numberly in 1 month.
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+
Bluecoders benchmark: across our permanent hires signed between August 2025 and August 2026, the Data, AI and ML family shows a median gross annual salary of €62,000 (7 hires). Full details and methodology in our 2026 tech and engineering salary barometer.
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 professional looking to discover new career opportunities? Don't miss our latest job openings.
Looking to hire a new team member for your company? We can help. Bluecoders specialises in tech recruitment. Contact us.
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 should you look for in a data engineer before hiring them?
Check 7 skills as a priority: advanced SQL and data modeling, orchestration and transformation (Airflow, dbt), mastery of batch or streaming depending on your need (Spark, Kafka), experience with a cloud data warehouse (Snowflake, BigQuery, Databricks), data quality and observability, solid software engineering fundamentals (Python, Git, CI/CD, tests), and governance (GDPR, access management). Test them on concrete cases: modeling a dataset, explaining how they'd recover a failed pipeline, commenting on a code snippet. On the soft skills side, the ability to prioritize business requests often makes the difference.
What technical test should you give a data engineer?
Favor a short exercise close to your reality: a modeling case built from 3 or 4 tables, a review of an existing pipeline that contains a data quality issue, or an architecture discussion about a flow your team actually operates. Avoid algorithm exercises disconnected from the role and multi-hour take-home tests, which discourage employed profiles. To structure the assessment, see our white paper 3 steps to evaluate a developer.
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?
On the tooling side, a Data Engineer works with SQL and NoSQL, orchestration and transformation tools like Airflow, Dagster, or dbt, distributed processing and streaming engines like Spark, Kafka, or Flink, and warehouses or lakehouses like Snowflake, BigQuery, Redshift, or Databricks, on AWS, Azure, or GCP. On the language side, Python and SQL dominate, with Java, Scala, or Go depending on the environment. The Hadoop ecosystem (Hive, HBase) is still present in some legacy environments.
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.