The table below provides summary statistics and salary benchmarking for jobs advertised in Liverpool requiring Data Mining skills. It covers vacancies from the 6 months leading up to 16 September 2025, with comparisons to the same periods in the previous two years.
6 months to 16 Sep 2025
Same period 2024
Same period 2023
Rank
51
-
-
Rank change year-on-year
-
-
-
Permanent jobs citing Data Mining
1
0
0
As % of all permanent jobs advertised in Liverpool
Data Mining falls under the Databases and Business Intelligence category. For comparison with the information above, the following table provides summary statistics for all permanent job vacancies requiring database or business intelligence skills in Liverpool.
Permanent vacancies with a requirement for database or business intelligence skills
39
69
87
As % of all permanent jobs advertised in Liverpool
9.82%
10.25%
18.59%
Number of salaries quoted
32
62
64
10th Percentile
£39,225
£31,250
£33,688
25th Percentile
£47,938
£35,250
£46,250
Median annual salary (50th Percentile)
£53,500
£51,250
£70,000
Median % change year-on-year
+4.39%
-26.79%
+33.33%
75th Percentile
£62,500
£88,750
-
90th Percentile
£77,825
£110,000
£78,375
Merseyside median annual salary
£53,000
£46,000
£66,250
% change year-on-year
+15.22%
-30.57%
+26.19%
Data Mining Job Vacancy Trend in Liverpool
Job postings citing Data Mining as a proportion of all IT jobs advertised in Liverpool.
Data Mining Salary Trend in Liverpool
Salary distribution trend for jobs in Liverpool citing Data Mining.
Data Mining Top 12 Co-occurring Skills and Capabilities in Liverpool
For the 6 months to 16 September 2025, job vacancies citing Data Mining also mentioned the following skills and capabilities in order of popularity.
The figures indicate the absolute number co-occurrences and as a proportion of all permanent job ads across the Liverpool region with a requirement for Data Mining.
Data Mining Co-occurring Skills and Capabilities in Liverpool by Category
The follow tables expand on the table above by listing co-occurrences grouped by category.
The same employment type, locality and period is covered with up to 20 co-occurrences shown in each of the following categories:
