The following table provides summary statistics for permanent job vacancies advertised in Carlisle with a requirement for Data Extraction skills. Included is a benchmarking guide to the salaries offered in vacancies that have cited Data Extraction over the 6 months to 28 April 2024 with a comparison to the same period in the previous 2 years.
Data Extraction is in the Processes and Methodologies category. The following table is for comparison with the above and provides summary statistics for all permanent job vacancies advertised in Carlisle with a requirement for process or methodology skills.
Permanent vacancies with a requirement for process or methodology skills
72
38
24
As % of all permanent jobs advertised in Carlisle
83.72%
97.44%
85.71%
Number of salaries quoted
68
29
8
10th Percentile
£26,250
£26,250
£32,597
25th Percentile
£37,000
£35,000
£39,620
Median annual salary (50th Percentile)
£47,500
£54,000
£47,000
Median % change year-on-year
-12.04%
+14.89%
+56.67%
75th Percentile
£65,000
£57,500
£52,813
90th Percentile
£71,500
£69,120
£72,125
Cumbria median annual salary
£42,500
£45,000
£35,000
% change year-on-year
-5.56%
+28.57%
-16.17%
Data Extraction Job Vacancy Trend in Carlisle
Job postings citing Data Extraction as a proportion of all IT jobs advertised in Carlisle.
Data Extraction Salary Trend in Carlisle
3-month moving average salary quoted in jobs citing Data Extraction in Carlisle.
Data Extraction Salary Histogram in Carlisle
Salary distribution for jobs citing Data Extraction in Carlisle over the 6 months to 28 April 2024.
Data Extraction Top 16 Co-occurring Skills and Capabilities in Carlisle
For the 6 months to 28 April 2024, job vacancies citing Data Extraction 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 Carlisle region with a requirement for Data Extraction.
Data Extraction Co-occurring Skills and Capabilities in Carlisle 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:
