Microsoft Azure SQL Database (formerly SQL Azure) Berkshire> Reading
The table below provides summary statistics and salary benchmarking for jobs advertised in Reading requiring Azure SQL Database skills. It covers permanent job vacancies from the 6 months leading up to 5 November 2025, with comparisons to the same periods in the previous two years.
Azure SQL Database 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 Reading.
Permanent vacancies with a requirement for database or business intelligence skills
82
153
224
As % of all permanent jobs advertised in Reading
16.87%
13.00%
22.27%
Number of salaries quoted
44
64
113
10th Percentile
£42,500
£38,950
£36,000
25th Percentile
£53,750
£50,313
£46,250
Median annual salary (50th Percentile)
£65,184
£62,500
£55,050
Median % change year-on-year
+4.29%
+13.53%
-15.31%
75th Percentile
£73,750
£82,813
£78,750
90th Percentile
£85,000
£109,500
£98,000
Berkshire median annual salary
£65,000
£67,500
£55,050
% change year-on-year
-3.70%
+22.62%
-15.31%
Azure SQL Database Job Vacancy Trend in Reading
Historical trend showing the proportion of permanent IT job postings citing Azure SQL Database relative to all permanent IT jobs advertised in Reading.
Azure SQL Database Salary Trend in Reading
Salary distribution trend for jobs in Reading citing Azure SQL Database.
Azure SQL Database Top 15 Co-Occurring Skills & Capabilities in Reading
For the 6 months to 5 November 2025, job vacancies citing Azure SQL Database also mentioned the following skills and capabilities in order of popularity.
The figures indicate the absolute number of co-occurrences and as a proportion of all permanent job ads across the Reading region with a requirement for Azure SQL Database.
Azure SQL Database Co-Occurring Skills & Capabilities in Reading by Category
The following tables expand on the one above by listing co-occurrences grouped by category. They cover the same employment type, locality and period, with up to 20 co-occurrences shown in each category:
