Data Engineer
Snapshot
Data engineers develop the architecture needed to process, manage, and store large amounts of data which will be used by data scientists for analysis. They design the infrastructure and maintain data pipelines and warehouses to leverage data for strategic advantage.
As a data engineer, your days will involve designing, building, and managing the infrastructure that supports data storage, processing, and analysis. You’ll work with large datasets, ensuring data quality, reliability, and accessibility for data scientists and other stakeholders. This role requires a blend of technical expertise, analytical thinking, and a focus on creating efficient and scalable data pipelines.
- • Designing and developing data pipelines to extract, transform, and load (ETL) data from various sources.
- • Building and maintaining data warehouses and data lakes to store and manage large datasets.
- • Ensuring data quality and integrity through validation and monitoring processes.
Where this occupation is in demand
Reported labour shortages and surpluses, by year. Published for occupation groups, not for individual job titles.
Figures cover Information and communications technology professionals — 75 jobs including this one.
In shortage: Austria, Belgium, Bulgaria, Cyprus and 6 more.
Longest-running shortage: Austria, 3 years.
Select a place on the map to see its figures.
About this source›
Source: ELA/EURES labour shortages and surpluses. Readings are published at occupation-group level, and cover Europe. Editions differ in annex layout and country coverage, so a change between years does not always mean the labour market changed. Countries in grey were not reported, which is not the same as being in balance.
What these words mean
The four things this section reports
- Reported demand
- Whether employers report needing people in this job — a judgement published by a national or EU body, not a count.
- Where it is heading
- Which way employment in this job is expected to move over the coming years, from an official projection.
- Openings
- Roughly how many openings arise — from growth and from people leaving the job.
- Typical pay
- What people in this job typically earn where the source publishes it. Blank does not mean unpaid; it means nobody publishes it for that place.
A measure is left out when nobody publishes it for that place, rather than shown as zero.
Which way the market leans for you
- In your favour
- More openings than people looking — employers are competing for candidates.
- Balanced
- Openings and candidates are roughly matched.
- Competitive
- More people looking than openings — expect to compete.
- Mixed evidence
- Sources disagree, or the same occupation group is short in one part and oversupplied in another.
Every source resolves to one of these four, so there is a single vocabulary to learn. What differs is the evidence behind it, which is printed underneath each verdict — a measured ratio of openings to jobseekers, or an assessment published by a national body.
How this job compares with other jobs in the same country
- Strong
- Among the strongest in that country
- Good
- Stronger than most jobs in that country
- Mixed
- About typical for that country
- Weak
- Weaker than most jobs in that country
This is a rank within one country, not a score you can carry across borders — the registers behind two countries count different people, so the same number means different things in each. It is also why a job can be among the strongest in a country and still show as Competitive: it leads the field in a market that is crowded overall.
Where these come from
Every figure is published by a national statistics office, a public employment service or an EU body, and each card names its source and the period it covers. Some places are counted monthly, others assessed once or twice a year, so two places on the same map can be describing different moments — the date is always shown.
None of this predicts one person’s chances. It describes a market.
Reported demand · Typical pay · Where it is heading (United States, Ireland, Australia, Malta, United Kingdom)
| Reported demand | Typical pay | Where it is heading | |
|---|---|---|---|
| United States | Not reported | $116,580 2025-05 | +8.2% 2024–2034 · BLS 2024-2034 |
| Ireland | Shortage reported ELA/EURES 2025 * | — | +32.3% 2023–2035 · Cedefop 2026 * |
| Australia | Assessed as balanced Jobs and Skills Australia 2025 * | — | — |
| Malta | Shortage reported ELA/EURES 2024 * | — | — |
| United Kingdom | Not reported | £59,593 2025-provisional | — |
* Published for occupation groups, not for individual job titles.
U.S. Bureau of Labor Statistics (OEWS)
Source: Office for National Statistics, Annual Survey of Hours and Earnings, Open Government Licence v3.0
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Future Outlook for data engineer
data engineer is entering a period of transformation. With a 42% exposure to AI tools, this role is not being replaced, it is evolving. Mastery of new digital tools will be the key to staying ahead.
How are these scores calculated?
The Resilience Score (0–100) estimates how structurally protected this occupation is from automation and AI disruption, based on task-level analysis. Higher scores mean more human-judgment-intensive tasks. AI Exposure shows the estimated percentage of task hours that current AI capabilities could affect. These are model-derived structural indicators, not predictions about individual job security.
How could data engineer change as AI adoption grows?
Several task areas may shift toward AI-assisted workflows, so reskilling becomes more important.
Illustrative scenario based on task automatability — not a forecast. Values are rounded the further ahead you look.
Several task areas may shift toward AI-assisted workflows, so reskilling becomes more important.
Illustrative scenario based on task automatability — not a forecast. Values are rounded the further ahead you look.
Model-based estimate. Use it as a planning signal, not a guarantee.
Compare this to your profile
How AI may change this role
Deterministic, model-based interpretation of current role signals — not a guarantee of replacement.
What still depends on people
Most tasks here are AI-assistable rather than purely human-led.
Where AI may become a co-pilot
- perform dimensionality reduction
- develop data processing applications
- manage ICT data architecture
Tasks most exposed to automation
- process data
- manage research data
- use data processing techniques
Vital Signs & AI Vectors
AI Exposure Vectors
0-100%Exposure to AI-assisted analysis, pattern recognition, and predictive modelling tasks
Exposure to content generation, creative augmentation, and large language model tools
Exposure to physical automation, robotics, and sensor-driven task displacement
Exposure to workflow automation, decision-support software, and process digitisation
Technical Details
NexFuture v3.0 estimates automation exposure natively from ESCO essential-skill groups, weighted by skill mass and calibrated against expert anchors. Scores are probabilistic estimates, not guarantees. See the NexFuture Methodology White Paper for full details.
Measures automation exposure. It does not measure pay, demand, or how many jobs exist near you.
What people in this role usually do
Digital Technology
A typical day as a data engineer
09 09:00 · Morning develop data processing applications
10 10:30 · Mid-morning design database in the cloud
12 12:00 · Midday establish data processes
14 14:00 · Afternoon implement data warehousing techniques
15 15:30 · Late afternoon manage data
17 17:00 · Wrap-up manage ICT data architecture
Task order is illustrative. Individual days vary.
What you need to do this work
The skills, knowledge and tools this role calls for — and the traits and rewards that come with it.
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use data processing techniques
Gather, process and analyse relevant data and information, properly store and update data and represent figures and data using charts and statistical diagrams.
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establish data processes
Use ICT tools to apply mathematical, algorithmic or other data manipulation processes in order to create information.
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use databases
Use software tools for managing and organising data in a structured environment which consists of attributes, tables and relationships in order to query and modify the stored data.
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manage quantitative data
Gather, process and present quantitative data. Use the appropriate programs and methods for validating, organising and interpreting data.
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store digital data and systems
Use software tools to archive data by copying and backing them up, in order to ensure their integrity and to prevent data loss.
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implement data warehousing techniques
Apply models and tools such as online analytical processing (OLAP) and Online transaction processing (OLTP), to integrate structured or unstructured data from sources, in order to create a central depository of historical and current data.
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manage research data
Produce and analyse scientific data originating from qualitative and quantitative research methods. Store and maintain the data in research databases. Support the re-use of scientific data and be familiar with open data management principles.
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create data sets
Generate a collection of new or existing related data sets that are made up out of separate elements but can be manipulated as one unit.
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manage data
Administer all types of data resources through their lifecycle by performing data profiling, parsing, standardisation, identity resolution, cleansing, enhancement and auditing. Ensure the data is fit for purpose, using specialised ICT tools to fulfil the data quality criteria.
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perform dimensionality reduction
Reduce the number of variables or features for a dataset in machine learning algorithms through methods such as principal component analysis, matrix factorization, autoencoder methods, and others.
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develop data processing applications
Create a customised software for processing data by selecting and using the appropriate computer programming language in order for an ICT system to produce demanded output based on expected input.
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manage ICT data architecture
Oversee regulations and use ICT techniques to define the information systems architecture and to control data gathering, storing, consolidation, arrangement and usage in an organisation.
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design database in the cloud
Apply design principles for an adaptive, elastic, automated, loosely coupled databases making use of cloud infrastructure. Aim to remove any single point of failure through distributed database design.
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process data
Enter information into a data storage and data retrieval system via processes such as scanning, manual keying or electronic data transfer in order to process large amounts of data.
cloud technologies
The technologies which enable access to hardware, software, data and services through remote servers and software networks irrespective of their location and architecture.
data models
The techniques and existing systems used for structuring data elements and showing relationships between them, as well as methods for interpreting the data structures and relationships.
data storage
The physical and technical concepts of how digital data storage is organised in specific schemes both locally, such as hard-drives and random-access memories (RAM) and remotely, via network, internet or cloud.
database management systems
The tools for creating, updating and managing databases, such as Oracle, MySQL and Microsoft SQL Server.
unstructured data
The information that is not arranged in a pre-defined manner or does not have a pre-defined data model and is difficult to understand and find patterns in without using techniques such as data mining.
SAS Data Management
The computer program SAS Data Management is a tool for integration of information from multiple applications, created and maintained by organisations, into one consistent and transparent data structure, developed by the software company SAS.
Teradata Database
The computer program Teradata Database is a tool for creating, updating and managing databases, developed by the software company Teradata Corporation.
- computer science
- data analytics
- statistics
Path to become a data engineer
What it typically takes to qualify: education level, where it is a regulated profession, and where to study.
Bachelor's or equivalent level
582 programmes in 23 countries teach the core skills of this job. Plus 139 short courses that build single skills.
A few examples, taught in the language of this page:
Higher Diploma in Science in Computing in Data Analytics
Atlantic Technological University · Ireland
Applied Data Science & Artificial Intelligence (VWO-track)
Breda University of Applied Sciences · the Netherlands
Diploma in Big Data for Business
Dublin Business School · Ireland
Data Science and Society
Tilburg University · the Netherlands
Higher Diploma in Data Analytics and Visualisation
University of Galway · Ireland
Certificate in Fundamentals of Data Analytics
Dublin Business School · Ireland
These are 6 of 721.
Sources
- 1. Source: Europass Qualification Dataset Register (European Commission)
- 2. Bron: DUO open onderwijsdata
Growth Pathways & Similar Roles
Explore typical career progression paths, adjacent skills, and similar roles to plan your next transition.
Where does data engineer fit?
Similarity scores based on skill overlap from ESCO data.
For organisations
The data on this page, working for your students, clients and staff.
Frequently asked questions
What skills are most important for a data engineer?
How does this role differ from a data scientist’s role?
I'm considering a career change. Is data engineering a good option?
How much does Data Engineer pay in the United States?
Sources: ESCO O*NET ELA/EURES Cedefop BLS Data updated October 3, 2026 About our data