Computer vision engineer to data quality specialist
A computer vision engineer already meets 37% of what the data quality specialist role asks for. The move turns on 11 required skills not yet in the profile.
66.1/ 100
1 How much of what a data quality specialist is expected to do is already part of a computer vision engineer’s work. Directional: the figure for the reverse move differs, because moving into a speciality is not the same problem as moving out of one. 2 Every skill the data quality specialist role is recorded as needing, required and optional together. What these numbers mean.
Why this move works
A data quality specialist role treats 9 of its required skills as things a computer vision engineer already does. You would walk in with these on day one.
- define data quality criteria
- establish data processes
- handle data samples
- implement data quality processes
- normalise data
- perform data cleansing
What stands in the way is 11 required skills the profile does not yet cover. Table 3 groups them; Table 4 says which to take first.
Table 2 · What you already bring
Of the 12 skills that carry over, these are the ones fewest other occupations ask for. A data quality specialist role needs them, and most people applying for one will not have them already. This is the part of a computer vision engineer background worth leading with.
- already held handle data samples information skills
- already held normalise data working with computers
- already held implement data quality processes working with computers
- already held perform data cleansing working with computers
- already held establish data processes working with computers
- already held data engineering information and communication technologies (icts)
All 12 carried skills, including the 9 the data quality specialist role treats as required.
Table 3 · What you may need to learn
The 28 missing skills fall into 8 areas of the ESCO skill hierarchy, numbered in the order worth working in. Areas carrying the most required skills come first, and inside each one the required skills sit above the optional ones. Nothing here is a verdict on you: it is a list of what this particular job asks for that your current one does not.
- still to learn manage data required
- still to learn manage database required
- still to learn process data required
- optional, not held execute ICT audits optional
- still to learn information structure required
- still to learn database required
- optional, not held LDAP optional
- optional, not held LINQ optional
- optional, not held MDX optional
- optional, not held N1QL optional
- optional, not held SPARQL optional
- optional, not held XQuery optional
- still to learn design database scheme required
- still to learn address problems critically required
- optional, not held design database in the cloud optional
- optional, not held build business relationships optional
- optional, not held train employees optional
- still to learn utilise regular expressions required
- still to learn use data processing techniques required
- optional, not held perform data analysis optional
- still to learn manage standards for data exchange required
- optional, not held manage schedule of tasks optional
- optional, not held perform project management optional
- still to learn data ethics required
- optional, not held visual presentation techniques optional
2 further areas in the appendix
Table 4 · Where to start
The 3 entries a data quality specialist role is least likely to hire without. The ordering is computed from the skill data, not from what pays.
- 01 utilise regular expressions practise this
- 02 design database scheme practise this
- 03 information structure study this
Each entry opens a course search for that skill. Career Overlap earns nothing from these links.
Appendix · The rest of the record
All 12 skills that carry over
- already held define data quality criteria skill
- already held establish data processes skill
- already held handle data samples skill
- already held implement data quality processes skill
- already held normalise data skill
- already held perform data cleansing skill
- already held query languages knowledge
- already held report analysis results skill
- already held resource description framework query language knowledge
- already held data engineering knowledge
- already held execute analytical mathematical calculations skill
- already held statistics knowledge
The 2 learning areas not shown above
- optional, not held data quality assessment optional
- optional, not held business processes optional
- optional, not held healthcare analytics optional
17 supplementary skills, helpful but not required
- optional, not held LDAP optional
- optional, not held LINQ optional
- optional, not held MDX optional
- optional, not held N1QL optional
- optional, not held SPARQL optional
- optional, not held XQuery optional
- optional, not held data quality assessment optional
- optional, not held design database in the cloud optional
- optional, not held execute ICT audits optional
- optional, not held healthcare analytics optional
- optional, not held visual presentation techniques optional
- optional, not held build business relationships optional
- optional, not held business processes optional
- optional, not held manage schedule of tasks optional
- optional, not held perform data analysis optional
- optional, not held perform project management optional
- optional, not held train employees optional
40 held skills the data quality specialist role does not ask for
- not needed by the target role Python (computer programming) knowledge
- not needed by the target role apply statistical analysis techniques skill
- not needed by the target role cognitive computing knowledge
- not needed by the target role computer graphics knowledge
- not needed by the target role computer programming knowledge
- not needed by the target role computer simulation knowledge
- not needed by the target role conduct literature research skill
- not needed by the target role conduct qualitative research skill
- not needed by the target role conduct quantitative research skill
- not needed by the target role conduct scholarly research skill
- not needed by the target role create data models skill
- not needed by the target role data science knowledge
- not needed by the target role debug software skill
- not needed by the target role deep learning knowledge
- not needed by the target role define technical requirements skill
- not needed by the target role deliver visual presentation of data skill
- not needed by the target role design user interface skill
- not needed by the target role develop computer vision system skill
- not needed by the target role develop data processing applications skill
- not needed by the target role develop software prototype skill
- not needed by the target role digital image processing knowledge
- not needed by the target role digital systems knowledge
- not needed by the target role digital twin technology knowledge
- not needed by the target role image formation knowledge
- not needed by the target role image recognition knowledge
- not needed by the target role integrated development environment software knowledge
- not needed by the target role interpret current data skill
- not needed by the target role machine learning knowledge
- not needed by the target role manage data collection systems skill
- not needed by the target role mathematical modelling knowledge
- not needed by the target role perform data mining skill
- not needed by the target role perform dimensionality reduction skill
- not needed by the target role principles of artificial intelligence knowledge
- not needed by the target role quantum computing knowledge
- not needed by the target role scientific computing knowledge
- not needed by the target role signal processing knowledge
- not needed by the target role state estimation knowledge
- not needed by the target role use markup languages skill
- not needed by the target role use software libraries skill
- not needed by the target role utilise computer-aided software engineering tools skill
13 gaps that are knowledge rather than practice
Knowledge gaps usually close through study. Practical skill gaps usually need something you can point at.
- still to learn information structure required
- still to learn data ethics required
- still to learn database required
- optional, not held LDAP optional
- optional, not held LINQ optional
- optional, not held MDX optional
- optional, not held N1QL optional
- optional, not held SPARQL optional
- optional, not held XQuery optional
- optional, not held data quality assessment optional
- optional, not held healthcare analytics optional
- optional, not held visual presentation techniques optional
- optional, not held business processes optional
Other moves recorded from computer vision engineer
Common questions about this move
Can a computer vision engineer become a data quality specialist?
Yes, and it is a substantial move. A computer vision engineer already covers 37% of what a data quality specialist is expected to do: 12 skills carry over, 9 of which the data quality specialist role treats as required.
What skills does a computer vision engineer need to become a data quality specialist?
11 required skills are missing, plus 17 optional ones. They fall into 8 areas of the ESCO skill hierarchy, led by information skills. The single most important is utilise regular expressions.
How hard is the move from computer vision engineer to data quality specialist?
Its learning distance is 66.1 out of 100, which is the substantial band. That accounts for both how much is missing and how specialised those skills are.
How this record was compiled
Both occupations are taken from ESCO, which lists the skills and knowledge each occupation is expected to have and marks every one required or optional. Nothing here is a prediction about hiring, and nothing here knows that a particular employer wants a particular certificate. Treat Table 3 as a starting point for your own research rather than a syllabus. The full method states what these figures can and cannot tell you.