Engineer Data Virtualization
972.233 kr. (DKK)/yr
467,42 kr. (DKK)/hr
42.000 kr. (DKK)/yr
The average engineer data virtualization gross salary in Odense, Denmark is 972.233 kr. or an equivalent hourly rate of 467 kr.. This is 6% lower (-61.666 kr.) than the average engineer data virtualization salary in Denmark. In addition, they earn an average bonus of 42.000 kr.. Salary estimates based on salary survey data collected directly from employers and anonymous employees in Odense, Denmark. An entry level engineer data virtualization (1-3 years of experience) earns an average salary of 682.852 kr.. On the other end, a senior level engineer data virtualization (8+ years of experience) earns an average salary of 1.098.100 kr..
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1.058.239 kr. (DKK)
9 %
Based on our compensation data, the estimated salary potential for Engineer Data Virtualization will increase 9 % over 5 years.
This chart displays the highest level of education for:
Engineer Data Virtualization, the majority at 100% with bachelors.
Typical Field of Study: Computer Programming, Specific Applications
See how education can impact your salaryThe cost of living in Odense, Denmark is 2% less than the average cost of living in Denmark. Cost of living is calculated based on accumulating the cost of food, transportation, health services, rent, utilities, taxes, and miscellaneous.
View Cost of Living PageOdense ( OH-dən-sə, US also OH-thən-sə; Danish: [ˈoðˀn̩sə] ) is the third largest city in Denmark (after Copenhagen and Aarhus) and the largest city on the island of Funen. As of 1 January 2025, the city proper had a population of 185,480 while Odense Municipality had a population of 210,803, making it the fourth largest municipality in Denmark (behind Copenhagen, Aarhus and Aalborg municipalities). Eurostat and OECD have used a definition for the Metropolitan area of Odense (referred to as a F
Sourced from WikipediaERI's compensation data are based on salary surveys conducted and researched by ERI. Cost of labor data in the Assessor Series are based on actual housing sales data from commercially available sources, plus rental rates, gasoline prices, consumables, medical care premium costs, property taxes, effective income tax rates, etc.
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