data engineer
Snapshot
Data engineers are the architects of the data world, building and maintaining the systems that allow organizations to harness the power of their information. If you enjoy problem-solving and creating robust, scalable solutions, a career as a data engineer could be a great fit.
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.
Data engineers are the architects of the data world, building and maintaining the systems that allow organizations to harness the power of their information. If you enjoy problem-solving and creating robust, scalable solutions, a career as a data engineer could be a great fit.
Could data engineer fit you?
Answer three quick questions. This is not a full assessment — it is a teaser to help you decide whether to compare your profile.
Do you enjoy tasks that require Analytical Thinking?
Do you enjoy tasks that require Achievement?
Do you enjoy tasks that require Attention to Detail?
Future Outlook for data engineer
The outlook for data engineer is exceptionally stable. While AI tools will assist with daily tasks, the core of this role relies on human judgment, resulting in a high resilience score of 75.4%.
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?
Human judgement, trust, and context remain strong protectors for this role.
How could data engineer change as AI adoption grows?
Human judgement, trust, and context remain strong protectors for this role.
How AI may change this role
Deterministic, model-based interpretation of current role signals — not a guarantee of replacement.
What still depends on people
This role remains strongly human-led where develop data processing applications depends on trust, nuance, and real-world judgement.
Where AI may become a co-pilot
AI is more likely to assist supporting tasks such as design database in the cloud, documentation, search, and workflow coordination.
Tasks most exposed to automation
Automation pressure appears selective rather than broad, with the strongest signal currently coming from AI / machine learning.
Detailed Analysis Vital Signs, AI Vectors & Megatrends
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Vital Signs, AI Vectors & Megatrends
Vital Signs
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 workflow automation, decision-support software, and process digitisation
Exposure to physical automation, robotics, and sensor-driven task displacement
Megatrend Signals
0-100%Model-derived scores. Indicates structural exposure to megatrends, not direct demand.
Technical Details
NexFuture™ v2.0 combines O*NET ability and activity profiles with ESCO skill group distributions and six global megatrend signals. Scores are probabilistic estimates, not guarantees. See the NexFuture™ Methodology White Paper for full details.
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.
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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.
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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.
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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.
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database management systems
The tools for creating, updating and managing databases, such as Oracle, MySQL and Microsoft SQL Server.
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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.
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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.
- computer science
- data analytics
- statistics
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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.
Skill DNA
Work personality traits and values that define this role
See whether this role fits your Career DNA
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Scan to continue on mobileGrowth 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.
Frequently asked questions
- What skills are most important for a data engineer?
- Strong programming skills (Python, Java, or Scala are common), experience with database technologies (SQL, NoSQL), knowledge of data warehousing concepts, and familiarity with cloud platforms (AWS, Azure, or Google Cloud) are crucial. Problem-solving abilities and a focus on automation are also highly valued.
- How does this role differ from a data scientist’s role?
- Data scientists focus on analyzing data to uncover insights and build predictive models. Data engineers build and maintain the infrastructure that enables data scientists to do their work. Think of it as the data scientist using the tools the data engineer builds.
- I'm considering a career change. Is data engineering a good option?
- Yes! The demand for data engineers is high, and the skills are transferable from various technical backgrounds. A strong foundation in programming and a willingness to learn new technologies are key for a successful transition.