Towards a space competencies taxonomy


  • This is the first competencies taxonomy to be developed specifically for the space sector.
  • It comprises about 250 competencies across five categories: traits, knowledge, technical skills, transferable skills, and qualifications.
  • It was constructed through analysis of 812 early career UK space job adverts from
  • It is a work in progress and is not yet comprehensive. We will continue to develop it as we conduct further research into the workforce needs of the sector.


The UK’s space sector is growing at a rate of more than 3% per annum, creating hundreds of new jobs each year 1b, and has set the goal that it should have a global market share of 10% by 2030 2.

However, UK space companies are facing a skills shortage. 68% predict that they will be hiring over the next 3 years, but already more than half of large organisations report being worried about having access to skilled workers, and nearly 40% of all organisations say staff recruitment is a major barrier to their growth 1c.

Though there is a lot of anecdotal evidence of skills shortages in particular areas 3, there is very little detailed data available for the sector on the relative demand for different skills. Understanding exactly which competencies the sector is lacking is vital in informing provision of training and approaches to recruitment

A taxonomy of competencies supports the process of recruitment, and facilitates upskilling and reskilling. It provides a common vocabulary for alignment between education providers, employers, and employees, and a common frame of reference against which we can measure demand for competencies, what competencies the space workforce already has, and the impact that new training courses might have.

With this in mind, we have developed an initial space competencies taxonomy. It is based on analysis of job adverts and builds on previous work including the draft Space Engineering Technician standards. As is to be expected, it has strong crossovers with other skills taxonomies, both general (such as Nesta’s and the Skills Builer Framework) and specific (such as the SFIA’s). This taxonomy is based on the language used by employers, an important factor because it allows us to identify and measure the demand for skills.

It is the first competencies taxonomy to be developed specifically for the space sector. This is important because the space sector is unusual in many ways, including having the most highly qualified workforce of any sector, with 75% of workers holding at least a first degree 1a and having a segmented structure of upstream and downstream. A specialised taxonomy helps ensure that the specific workforce needs of the sector can be properly addressed.

The taxonomy we present here is a work in progress and is not yet comprehensive. We will continue to develop it as we conduct further research into the workforce needs of the sector.

Though the sector normally talks about ‘skills’ shortages, in the rest of this document we use the word ‘competencies’ instead so that we can talk about traits, knowledge, skills, and qualifications separately.

Taxonomy structure


The taxonomy has a hierarchical structure with up to five levels.

First level: competency types

At the top level are five categories. These are comparable with other definitions such as Engineering UK’s five ‘components of employability’ 4 and the CBI’s definition of employability 5 and similar to the Civil Service Success Profiles Framework 6:

1. Traits

We define traits as features of a person’s character or personality, embodied by certain behaviours. They encompass what is typically described as ‘character’ in education contexts 78, as well as interests (such as interest in space) and personal preference (such as willingness to travel).

While they are distinct from transferable skills as they are about mindset and temperament, they should not be considered as innate or unlearnable. They can, in most cases, be learned, but are typically developed over a longer time period and at an earlier stage of development than transferable skills.

In conversation they would typically be prefixed with ‘You are’. Examples include: ‘responsible’, ‘hardworking’, and ‘willing to travel’.

2. Knowledge

We define knowledge as facts or information about a given topic. It is often difficult to separate knowledge from skills, and in these cases we usually err towards classifying things as skills.

In conversation knowledge would typically be prefixed with ‘You know about’ or ‘You have experience of’. Examples include: ‘Engineering standards’ and ‘STEM Principles’. Foreign languages are also classified as knowledge, separate from communication skills.

3. Technical skills

We define technical skills as skills which are specific to a sector or job.

In conversation they would typically be prefixed with ‘You can’ or ‘You have’. Examples include: ‘Use CAD software’ and ‘Contribute to technical reviews’.

4. Transferable skills

We define transferable skills as skills which can be applied in a similar way to most jobs, regardless of sector. Elsewhere they may be referred to as ‘essential skills’, ‘soft skills’, or ‘transversal skills'.

In conversation they would typically be prefixed with ‘You can’ or ‘You have’. Examples include: ‘Work in a team’ and ‘Attention to detail’.

5. Qualifications

We define qualifications as formal recognitions of certain knowledge or skills following an assessment process by a relevant body such as a college, university, or professional development organisation.

In conversation they would typically be prefixed with ‘You have’. Examples include: ‘Driving license’ and ‘PhD’.

Second level: thematic areas

At the second level are broad thematic areas such as ‘Science & Engineering’, ‘Programming & Computer Science’, and ‘Marketing & PR’.

Third level: competencies

At the third level are the specific competencies. For knowledge competencies, these are topics like ‘Orbital mechanics’; for skills competencies these are specific skills like ‘Perform spectroscopy’.

Fourth level and below: subcompetencies

The fourth and lower levels are for adding specificity to the competency definition. For example: Process and analyse data (L3 competency) > Geospatial data (L4 subcompetency) > GNSS data (L5 subcompetency). Many competencies do not have subcompetencies at present, but might do in future iterations of the taxonomy. They are particularly useful for specifying software packages (eg. IT skills > MS Office > Excel).

Classification codes

Each item in the taxonomy has a unique classification code, similar to those used in the Dewey Decimal Classification system used by libraries.

Each code consists of a number of digits separated by decimal points. The first digit represents the first level of the hierarchy described above, and so on. For example, the code ‘’ represents the following:
3 - Technical skills
  2 - Programming & Computer Science
    1 - Process and analyse data
      1 - Geospatial data
        1 - GNSS data

In this way we are able to be very specific when identifying competencies for jobs, whilst also getting a clear picture of the higher level competencies that are needed across the sector.


In addition to the taxonomy proper, we have identified certain groupings of competencies such as ‘European languages’ and ‘ESA member state languages’ which do not sit in the hierarchy but are still used in job adverts. These groups map to a set of competencies such as ‘French’, ‘Spanish’, ‘German’.



To develop our taxonomy, we analysed job adverts posted on between December 2015 and December 2019. is a British space jobs board operated by UKSEDS, the UK’s national student space society, which focuses on internships, graduate schemes, PhDs, postdoctoral fellowships, and other early careers opportunities. The cutoff criterion is that positions should ask for no more than three years of experience.

The initial dataset contained just under 1300 adverts. We cleaned this dataset by removing duplicate adverts, and excluding all adverts which were not for internships, graduate positions, or direct entry jobs. This left us with 812 adverts. The limitations of this dataset are discussed in the Limitations section.

Taxonomy development

We reviewed 104 adverts (~13% of total) by hand and tagged mentions of competencies such as ‘Python’ or ‘problem solving’, linking them to a taxonomy entry for the competency. For example, ‘communication and presentation skills’ and ‘written and verbal communication’ might both be linked to the taxonomic entry of ‘communication skills’. In this way we built up a library of natural language examples of competencies in our taxonomy.

A screenshot of the tagging system. On the left is text from a job advert with certain words and phrases highlighted. On the right is a list of those words and which tag in the taxonomy they relate to.
A sample job advert with tags

We then compared this library to the entire dataset, identifying which competencies were mentioned in each job advert.

Where no competencies were identified within a job advert, we reviewed the advert manually and added any missing competencies.


We assessed the accuracy of our algorithm by manually checking an additional randomly selected 50 job adverts and identifying any tags that had been missed or added incorrectly by the tagging algorithm.

Across the 50 jobs there were 468 tags in 383 categories. We found that 5 (1.3%) of these categories were incorrect – for example an advert for an administrative role described the employer as an ‘electronics’ company, and the job was incorrectly classified as requiring knowledge of electronics. We also found that there were 30 tags (6.4%) that we would have applied but had not been by the algorithm – for example we would have tagged ‘autonomous and challenge-driven’ as an example of the skill of working independently.


  1. 1: Traits

    1. 1.0: Traits

      1. 1.0.1: Creative / innovative

      2. 1.0.2: Enthusiastic

      3. 1.0.3: Flexible

      4. 1.0.4: Inquisitive / curious

      5. 1.0.5: Open-minded / willing to learn

      6. 1.0.6: Practical / hands on

      7. 1.0.7: Resilient

      8. 1.0.8: Resourceful / proactive

      9. 1.0.9: Responsible / trustworthy

      10. 1.0.10: Self-improving

      11. 1.0.11: Interested in space

      12. 1.0.12: Willing to travel

      13. 1.0.13: Self-motivated

      14. 1.0.14: Hardworking

  2. 2: Knowledge

    1. 2.1: Science & Engineering

      1. 2.1.1: Adhesives, bonding, soldering and fastening techniques required to meet space qualification standards.

      2. 2.1.2: Configuration and Document Management Control Processes including issue control, incorporation of change and End Item Data Pack.

      3. 2.1.3: CubeSats

      4. 2.1.4: Disciplines and handling in cleanliness and contamination-controlled environments.

      5. 2.1.5: Ground Support Equipment and Systems including electrical/electronic test equipment and mechanical handling equipment.

      6. 2.1.6: Imaging detectors

      7. 2.1.7: Launch vehicle design

      8. 2.1.8: Mechanical, Electrical and Electronic Analysis & Testing principles, including space industry-specific test standards.

      9. 2.1.9: RF/Microwave systems

      10. 2.1.10: Orbital Mechanics

      11. 2.1.11: Planetary science

      12. 2.1.12: Principles of Additive Manufacturing for application in space including powder quality and repeatability of build.

      13. 2.1.13: thermal-vacuum, electromagnetic compatibility, shock, vibration and acoustic testing.

      14. 2.1.14: Properties, handling and application of space qualified materials including Electrostatic Discharge (ESD) precautions.

      15. 2.1.15: Purpose of approved processes, components, parts and materials lists and verification control documentation.

      16. 2.1.16: Quality and Product Assurance principles

      17. 2.1.17: Radar

      18. 2.1.18: Space sector

        1. Relationships between customers, partners & suppliers in the international space engineering and manufacturing sector.

      19. 2.1.19: Remote sensing

      20. 2.1.20: Robotics

      21. 2.1.21: Satellite communication systems

      22. 2.1.22: Software Defined Radio (SDR)

      23. 2.1.23: AIT such as vibration, thermal-vacuum, electromagnetic compatibility.

      24. 2.1.24: Spacecraft operations

      25. 2.1.25: Spacecraft Systems

      26. 2.1.26: STEM Principles

        1. Precision and uncertainty in measurement systems, including limitations and appropriate use.

      27. 2.1.27: The space environment including thermal, vacuum, radiation, atomic oxygen and launch operations.

      28. 2.1.28: Vacuum and pressurised systems and measurement.

      29. 2.1.29: Engineering Standards

        1. ECSS

        2. GSWS

        3. CCSDS

      30. 2.1.30: Concurrent design

      31. 2.1.31: Knowledge of control systems

      32. 2.1.32: Knowledge of electronics

      33. 2.1.33: Knowledge of ESA

      34. 2.1.34: Knowledge of mechanical design

      35. 2.1.35: Knowledge of optics

      36. 2.1.36: Knowledge of science

      37. 2.1.37: Knowledge of Synthetic Aperture Radar (SAR)

      38. 2.1.38: Knowledge of tribology

      39. 2.1.39: Design of experiments

      40. 2.1.40: Principles of thermodynamics

    2. 2.2: Programming & Computer Science

      1. 2.2.1: Embedded programming

        1. Arduino

        2. FPGA

        3. Microprocessors

      2. 2.2.2: Encryption/cryptography

      3. 2.2.3: Networking, Ethernet

      4. 2.2.4: Software Standards

        1. DO178-B

        2. IoT protocols and standards

        3. MISRA

        4. UML

      5. 2.2.5: Image processing

      6. 2.2.6: Machine learning

    3. 2.3: Languages

      1. 2.3.1: French

      2. 2.3.2: German

      3. 2.3.3: Italian

      4. 2.3.4: English

      5. 2.3.5: Mandarin

    4. 2.4: Education & Outreach

      1. 2.4.1: Outreach

      2. 2.4.2: Teaching experience

      3. 2.4.3: Knowledge of the national curriculum

      4. 2.4.4: Experience with children

    5. 2.5: Work Experience

      1. 2.5.1: Research environment experience

  3. 3: Technical Skill

    1. 3.1: Science & Engineering

      1. 3.1.1: Work in facilities such as cleanrooms, workshops and testing facilities

      2. 3.1.2: Assemble, integrate and test at equipment, subsystem and system level.

      3. 3.1.3: Electronics AIT

      4. 3.1.5: Contribute to technical reviews such as assembly, integration and test readiness, and non-conformance reviews.

      5. 3.1.6: Contribute to the definition of space engineering process improvement plans.

      6. 3.1.7: Design electronics

      7. 3.1.8: Do aerodynamic analysis (use CFD software)

      8. 3.1.9: Do flight analysis

      9. 3.1.10: Do structural analysis (use FEA software)

        1. Abaqus

      10. 3.1.11: Do thermal analysis

      11. 3.1.12: Inspect electrical, mechanical or electronic equipment for quality assurance purposes.

      12. 3.1.13: Interpret outputs from manufacturing software such as Computer Aided Design (CAD) / Computer Aided Manufacture (CAM) and Product Data Management / Product Lifecycle Management (PDM/PLM)

      13. 3.1.14: Measure, test and analyse, using instruments such as pressure gauges, micrometers, balances and non-contact approaches.

      14. 3.1.15: Perform appropriate joining techniques

      15. 3.1.16: Perform electrical and electronic measurement and testing

      16. 3.1.17: Perform spectroscopy

      17. 3.1.18: Prepare and complete documentation and specifications

      18. 3.1.19: Support and maintain ground support systems for spacecraft and subsystems.

      19. 3.1.20: Use and maintain cryogenic systems for space applications

      20. 3.1.21: Use and maintain vacuum and pressure systems for space applications (such as environmental test chambers, pressure-fed propulsion systems, and gas supply lines for manufacturing & testing) including associated processes and documentation such as Piping & Instrumentation Diagrams.

      21. 3.1.22: Use CAD software

        1. Solidworks

        2. Siemens NX

        3. CATIA

        4. ProEngineer

        5. Autodesk Inventor

        6. SolidEdge

        7. EagleCAD

        8. CREO

        9. KiCad

        10. LTspice

      22. 3.1.23: Use GIS software

        1. SaVoir

        2. PCI Geomatica

        3. ENVI

        4. ArcGIS

      23. 3.1.24: Use internal and external Quality Management Systems

      24. 3.1.25: Use CAE software

      25. 3.1.26: Conduct practical engineering activities safely

      26. 3.1.27: Laboratory skills

      27. 3.1.28: Perform research

      28. 3.1.29: Design mechanical parts

      29. 3.1.30: Interpret specifications

    2. 3.2: Programming & Computer Science

      1. 3.2.1: Process and analyse data

        1. Geospatial data

          1. GNSS data

          2. Satellite imagery

      2. 3.2.3: Design, develop, and deploy software

        1. C/C++

        2. C#

        3. CSS

        4. Django

        5. FOTRAN

        6. HTML

        7. IDL

        8. Java

        9. JavaScript

        10. Linux

        11. Matlab

          1. Simulink

        12. Python

        13. Ruby

        14. VBA

        15. Visual Basic

        16. XML

        17. .NET

        18. Acceleo

        19. Ecore

        20. GoLang

        21. JSON

        22. Solidity

        23. STK

      3. 3.2.4: Use cloud computing platforms

        1. AWS

        2. Docker

      4. 3.2.5: Design and use databases

        1. SQL

        2. PostgresSQL

        3. Microsoft SQL Server

      5. 3.2.6: Use version control systems

        1. Git

      6. 3.2.7: Data collection

      7. 3.2.8: Front-end development

    3. 3.3: Creative design

      1. 3.3.1: GUI design

      2. 3.3.2: Use creative software

        1. Adobe CC

          1. Photoshop

          2. InDesign

          3. Premier

      3. 3.3.3: Multimedia

        1. Video

        2. Audio

      4. 3.3.4: Illustration skills

    4. 3.4: Marketing & PR

      1. 3.4.1: Social media

      2. 3.4.2: Media

        1. Press releases

    5. 3.5: Sales

    6. 3.6: Education & Outreach

      1. 3.6.1: Deliver planetarium shows

      2. 3.6.2: Teaching and lecturing

    7. 3.7: Finance

      1. 3.7.1: Use accounting software

        1. Netsuite

    8. 3.8: Astronomy photography

    9. 3.9: Business development

  4. 4: Transferable Skill

    1. 4.0: Transferable Skill

      1. 4.0.1: Analytical skills

      2. 4.0.2: Attention to detail

      3. 4.0.3: Communication skills

        1. Communicate verbally

          1. Presenting / Public speaking

        2. Write

          1. Technical report writing

        3. Listen

      4. 4.0.4: Interpersonal skills

        1. Be polite

        2. Work in a team

        3. Customer/partner relationships

        4. Negotiate and influence

        5. Lead a team

      5. 4.0.5: IT skills

        1. Eclipse software

        2. MS Office

          1. Outlook

          2. Excel

          3. Word

          4. PowerPoint

          5. Project

          6. Visio

          7. Publisher

        3. CRM system

          1. SalesForce

        4. ERP system

        5. Operating systems

          1. Windows

          2. Linux

          3. OS X

        6. Using databases

        7. Project management software

          1. MS Project

      6. 4.0.6: Planning & organisation

        1. Event planning

        2. Process management

      7. 4.0.7: Problem solve

        1. 8D

        2. FMEA

        3. PDCA

      8. 4.0.8: Time management skills

      9. 4.0.9: Work independently

      10. 4.0.10: Work under pressure

      11. 4.0.11: Administer First Aid

      12. 4.0.12: Conduct risk assessments

      13. 4.0.13: Coach/mentor someone

      14. 4.0.14: Keep records

      15. 4.0.15: Manual dexterity

      16. 4.0.16: Numerical skills

      17. 4.0.17: Calm in a crisis

      18. 4.0.18: Follow health and safety guidelines

  5. 5: Qualifications

    1. 5.0: Qualifications

      1. 5.0.1: Other

        1. Driving license

      2. 5.0.2: Project management

        1. AgilePM

        2. APM

        3. ITIL

        4. PRINCE2

    2. 5.1: Science & Engineering

      1. 5.1.1: Spacecraft Control

        1. CAT2 Spacecraft Controller

Future work

The taxonomy we present here is a work in progress and is not yet comprehensive. We will continue to develop it as we conduct further research into the workforce needs of the sector.

Most importantly, we want to improve its coverage. There are undoubtedly many skills that are missing because they did not appear in the jobs we analysed to produce the taxonomy. Analysing more jobs, particularly from other datasets will help to ensure that the taxonomy is a good representation of the skills needed across the sector.

We also intend to add definitions to each of the competencies identified in our taxonomy, creating a resource for the space sector which supports the process of recruitment, and facilitates upskilling and reskilling. Our goal is to have a common vocabulary for alignment between education providers, employers, and employees, and a common frame of reference against which we can measure demand for competencies, what competencies the space workforce already has, and the impact that new training courses might have.

We recognise that there are many other relevant taxonomies with which our work overlaps, and we want to identify and map these links so that the different taxonomies can be used together and do not needlessly duplicate each other.


Dataset limitations

Early careers bias

The SCUK dataset is made up of adverts for early career space jobs. The competencies demanded for early career jobs may be different from the demand across the sector more generally. In future work we will look at wider datasets to examine this in more detail.

Inclusion of non-UK jobs

Though we present our analysis as being an assessment of skills demand in the UK space sector, about 30% of the jobs in the SCUK dataset are based outside of the UK. Of these, most (28%) are based in Europe, primarily Germany (10%) and the Netherlands (4%). advertised these roles as they were open to British nationals, and we include them as many relate to work in which the UK is closely involved, or to agencies such as ESA of which the UK is a member country.

There is no indication that the demands of the European space sector are significantly different from those of the UK’s, and many of these roles may need to be duplicated in the UK after Brexit if the UK develops replacement for EU programmes such as Galileo 9.

UK regional bias

The SCUK dataset has a significant bias toward companies in the South East of England and Scotland at the expense of London, the South West, and the East of England compared to data from the UK Space Agency’s Size and Health report 1d.

Distribution of jobs in the SCUK dataset and employees in the Size and Health report by UK region.
RegionSCUK Dataset
(% of jobs)
S& H 2018
(% of employees)
North East0.22.2
North West1.65.6
Yorkshire and the Humber0.33.1
East Midlands5.22.1
West Midlands0.22.8
East of England5.710.5
South East53.921.6
South West9.43.2
Northern Ireland00.3
Other (includes jobs with no fixed location)5.20

It is not clear why this is the case, but as a result care should be taken when considering our findings in a regional context. We have chosen not to segment our results by region because the sample size for some regions is too small to be a useful indication of demand in those regions.

Upstream bias

The SCUK dataset is significantly biased toward upstream companies compared to data from the UK Space Agency’s Size and Health report 1b.

Distribution of jobs by segment in the SCUK dataset and the Size and Health report.
SegmentSCUK Dataset
% of jobs
S& H 2018
% of jobs

It is not clear why this is the case. One possibility is that upstream companies tend to be larger with well developed internship programmes and graduate intakes, while downstream companies tend to be smaller and less able to take on a large number of early career employees. Additionally, the Space Placements in Industry (SPIN) programme which places a large number of undergraduate and postgraduate students in primarily downstream summer internships is only represented in the SCUK data only as a single post per year and not as multiple individual posts.

As a result, where there is a significant variation between upstream and downstream demand for a competency (such as for data analysis skills), the ‘all jobs’ figure should not be considered an accurate picture of sector-wide demand, and instead demand should be looked at on a sector-specific basis.

Individual job bias cautioned that some schemes with large numbers of openings – for example the ESA Young Graduate Trainee Programme – are represented by only a single advert per year.

Taxonomy deficits

Omission of niche skills

Our approach to developing the taxonomy means that certain niche skills that are demanded for only a few jobs, but are nonetheless important to the sector, may have been omitted. We intend for the taxonomy to be a living database that is continuously improved upon, and hope to include any omitted skills in future iterations.


We are grateful to UKSEDS for sharing their data from, without which this work would not have been possible. We would also like to extend a special thank you to Portia Bowman and Rob Garner for their advice and ideas, to James Telfer and Jacob Smith for proofing this paper, to our advisors Ed Chester, Julia Hunter-Anderson, and Sheila Kanani, and to Nigel Bannister and Roy Haworth for their suggestions and support in developing the competency taxonomy.


This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You can copy, redistribute, and adapt what we’ve presented for any non-commercial purpose. However, you must give us credit and link back to this page. If you want to use it in a commercial context, get in touch with us.


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