knowledge engineer
Snapshot
Are you fascinated by how humans solve complex problems and want to build systems that mimic that expertise? As a knowledge engineer, you'll be at the forefront of integrating human knowledge into computer systems, enabling organizations to tackle challenges with greater efficiency and intelligence.
Knowledge engineers are vital in bridging the gap between human expertise and artificial intelligence. Your work involves extracting, structuring, and maintaining knowledge within computer systems – often called knowledge bases. This allows organizations to automate tasks, improve decision-making, and solve problems that typically require a high level of human skill. You’ll be designing and building intelligent systems that leverage this knowledge, constantly refining them to ensure accuracy and effectiveness. This role sits within a leadership and strategy career band, requiring both technical proficiency and strategic thinking.
- • Elicit and extract knowledge from various sources, including documents, databases, and subject matter experts.
- • Design and implement knowledge representation techniques, such as rules, frames, semantic nets, and ontologies, to structure information effectively.
- • Build and maintain knowledge bases, ensuring data accuracy, consistency, and accessibility.
Where this occupation is in demand
Reported labour shortages and surpluses, by year. Published for occupation groups, not for individual job titles.
Deeper colour: reported the same way in more consecutive years.
Figures cover Information and communications technology professionals — 75 jobs including this one.
In shortage: Austria, Bulgaria, Czechia, Denmark 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.
Explore More
Find your career path and explore the science behind our recommendations.
Could knowledge 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 Cooperation?
Do you enjoy tasks that require Achievement?
Future Outlook for knowledge engineer
The outlook for knowledge engineer reflects a balanced mix of automation exposure and durable, human-led work.
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 knowledge 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.
How could knowledge 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.
How AI may change this role
Deterministic, model-based interpretation of current role signals — not a guarantee of replacement.
What still depends on people
- assess ICT knowledge
- manage business knowledge
- define technical requirements
Where AI may become a co-pilot
- analyse business requirements
- use an application-specific interface
- use markup languages
Tasks most exposed to automation
- create semantic trees
- manage database
- use databases
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 workflow automation, decision-support software, and process digitisation
Exposure to physical automation, robotics, and sensor-driven task displacement
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 knowledge engineer
09 09:00 · Morning assess ICT knowledge
10 10:30 · Mid-morning create semantic trees
12 12:00 · Midday manage ICT semantic integration
14 14:00 · Afternoon use an application-specific interface
15 15:30 · Late afternoon apply ICT systems theory
17 17:00 · Wrap-up use markup languages
Task order is illustrative. Individual days vary.
-
business process modelling
The tools, methods and notations such as Business Process Model and Notation (BPMN) and Business Process Execution Language (BPEL), used to describe and analyse the characteristics of a business process and model its further development.
-
database development tools
The methodologies and tools used for creating logical and physical structure of databases, such as logical data structures, diagrams, modelling methodologies and entity-relationships.
-
information extraction
The techniques and methods used for eliciting and extracting information from unstructured or semi-structured digital documents and sources.
-
information structure
The type of infrastructure which defines the format of data: semi-structured, unstructured and structured.
-
natural language processing
The technologies which enable ICT devices to understand and interact with users through human language.
-
principles of artificial intelligence
The artificial intelligence theories, applied principles, architectures and systems, such as intelligent agents, multi-agent systems, expert systems, rule-based systems, neural networks, ontologies and cognition theories.
- business intelligence
- data engineering
- data science
-
manage business knowledge
Set up structures and distribution policies to enable or improve information exploitation using appropriate tools to extract, create and expand business mastery.
-
define technical requirements
Specify technical properties of goods, materials, methods, processes, services, systems, software and functionalities by identifying and responding to the particular needs that are to be satisfied according to customer requirements.
-
apply ICT systems theory
Implement principles of ICT systems theory in order to explain and document system characteristics that can be applied universally to other systems
-
manage ICT semantic integration
Oversee integration of public or internal databases and other data, by using semantic technologies to produce structured semantic output.
-
use an application-specific interface
Understand and use interfaces particular to an application or use case.
-
use markup languages
Utilise computer languages that are syntactically distinguishable from the text, to add annotations to a document, specify layout and process types of documents such as HTML.
-
assess ICT knowledge
Evaluate the implicit mastery of skilled experts in an ICT system to make it explicit for further analysis and usage.
-
manage database
Apply database design schemes and models, define data dependencies, use query languages and database management systems (DBMS) to develop and manage databases.
-
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.
-
analyse business requirements
Study clients' needs and expectations for a product or service in order to identify and resolve inconsistencies and possible disagreements of involved stakeholders.
Skill DNA
Work personality traits and values that define this role
See whether this role fits your Career DNA
Take the free Career DNA assessment to see how knowledge engineer aligns with your interests, work style, and future path. In less than 10 minutes, you will get a personalized fit signal and a roadmap for what to do next.
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 knowledge engineer fit?
Similarity scores based on skill overlap from ESCO data.
Frequently asked questions
- What kind of background is helpful for becoming a knowledge engineer?
- A strong foundation in computer science, data science, or a related field is beneficial. Familiarity with knowledge representation techniques, programming languages (like Python or Java), and database management systems is also crucial. Experience with artificial intelligence concepts and methodologies is a plus.
- How does this role differ from a data scientist?
- While both roles involve data, knowledge engineers focus specifically on structuring and representing *explicit* knowledge – the kind of knowledge that can be articulated and codified. Data scientists often work with broader datasets and focus on uncovering patterns and insights through statistical analysis and machine learning.
- What are the key work styles and values that contribute to success in this role?
- Success requires analytical thinking, attention to detail, and a strategic mindset (1.C.7.b, 1.C.3.a, 1.C.6). You’ll need to be comfortable working independently and collaboratively (1.C.5.b, 1.C.1.a), and driven by a desire for achievement, innovation, and contributing to organizational goals (1.B.2.a, 1.B.2.b, 1.B.2.c, 1.B.2.f).
- Knowledge Engineer — is there a shortage in Europe?
- Yes. In the 2025 ELA/EURES edition, a shortage was reported in 10 of the 11 European countries that assessed this occupation group: Austria, Bulgaria, Czechia, Denmark and 6 more. Austria has reported one for 3 consecutive years. These assessments are published per occupation group rather than per job title.
- Knowledge Engineer — what does it pay in the United States?
- $112,590 a year at the median, as of 2025-05. State medians run from $69,490 to $163,350. Source: US Bureau of Labor Statistics. This is a United States figure and not a projection for Europe.