Salary by Profession and Country Over Time

Salary Differences by Country and Profession

By Kelly Garrett [source]

About this dataset

This dataset contains survey responses from 882 data professionals from 46 countries who took part in the 2021 Global Data Professional Salary Survey. Our goal was to understand how much database administrators, data analysts, data architects, developers and data scientists make across the world in 2017-2021.

The survey covers three years of salary trends, allowing you to compare and contrast movements over time. It also includes an optional postal code field which can be used to identify global regions with specific salary trends. In addition, all questions asked this year were also asked in 2017 and 2018 so that you can easily track changes in compensation over three years.

The spreadsheet contains anonymized responses which are provided as public domain making it available for any purpose without attribution or mention of anyone else. With this dataset at your disposal you'll have access to the detailed salary information needed to make informed decisions about your career development!

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How to use the dataset

• Start by familiarizing yourself with the columns in this dataset. The columns range from age of respondent to country of residence. It also includes salary information for each year (average annual income for 2017, 2018, and 2019). Read through each column header carefully to understand what you're looking at.

• Explore some basic summary statistics about the sample group such as median salary levels by profession or average age by nationality are interesting ways to get acquainted with this data set quickly. Excel's native statistical tools may be used here if you're using an excel file version as your source material; otherwise, you can use any programming language or statistics software that supports importing an exportable CSV (Comma Separated Values) format file or conversion thereof into something manipulable form like a spreadsheet or table structure within your preferred platform..

• You'll then want to identify which factors might be influencing salaries such as experience level, gender and geographical location etc., and attempt some correlation testing between those features against salaries across different job roles or countries over time - where possible without having external datasets available terms of area data points matching up perfectly between thematic dimensions presented within the Respondents' Survey Results tab.. Subsets may also prove relevant when carrying out deeper statistical testing—for example isolating particular participation sets like Ireland alone versus looking at just Europe/Middle East/Africa region altogether..

• Finally look at how these factors have changed over time - it's worth bearing in mind that seasonality might play a role here too depending on where respondents originally reside so it could still be relevant if larger trends towards comparing yearly cohorts differs more widely than expected based purely national economic condition context changes during particular quarters throughout those periods tracked in our findings report � comparison purposes if looking country-by-country instead just individual profiles without taking overall stimulant effects into account e.g higher education qualifications among ~2 yr cohorts vs ~3 yr ones across different populations: Comparing annual amounts doled out employers making ultra-quick transitioning easier tracking changes alone isn't feasible because they're normalized

Research Ideas

• Analyzing regional salary gaps amongst data professionals within the same country, or between countries.
• Evaluating trends in salary rates over time by reviewing changes in year over year responses.
• Generating employer profiles by comparing the salary range of employees at different organizations and industries, as well storing demographic info of individuals who participated in the survey (i.e age range, gender etc)

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2019_Data_Professional_Salary_Survey_Responses.csv

File: Data_Professional_Salary_Survey_Responses.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Kelly Garrett.

Gross weekly and hourly earnings by level of occupation, UK, quarterly, not seasonally adjusted. Labour Force Survey. These are official statistics in development.

This Dataset indicates average salary by position title and grade for full-time regular employees. Data excludes elected, appointed, non-merit and temporary employees. Underfilled positions are also excluded from the dataset. Update Frequency : Annually

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

As of May 2024, the annual mean wage for management occupations in the United States stood at 141,760 U.S. dollars, making it the highest among the twenty largest occupational groups. In contrast, office and administrative support occupations represented the largest occupational group in terms of employment, with an annual mean wage of 50,161 U.S. dollars.

Detailed labour market outcomes by educational characteristics, including detailed occupation, hours and weeks worked and employment income.

Graph and download economic data for Income Before Taxes: Self-Employment Income by Occupation: Wage and Salary Earners: Technological, Sales, and Clerical Workers (CXUSFEMPINCLB1205M) from 1984 to 2024 about self-employed, clerical workers, occupation, salaries, tax, wages, sales, income, and USA.

Spreadsheet of employment and salary data by job by city, state, and industry. View Occupational Employment and Wage Statistics from the Bureau of Labor Statistics.

The statistic shows the mean earnings of male full-time, year-round workers in the U.S. in 2010, by occupation. The median earnings of workers in service occupations were 31,433 U.S. dollars .

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Management, professional, and related occupations: 16 years and over (LEU0254524600Q) from Q1 2000 to Q3 2025 about second quartile, management, occupation, professional, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

In 2024, many of the highest paying jobs were those in the medical field. The mean annual pay for psychiatrists was at 269,120 U.S. dollars in the United States. The highest paying occupation was pediatric surgeon, with a mean annual wage of 450,810 U.S. dollars.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Miscellaneous agricultural workers occupations: 16 years and over (LEU0254558000A) from 2000 to 2025 about second quartile, miscellaneous, occupation, full-time, agriculture, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

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Companies' Salary Dataset for Various Job Roles/Titles

This salary dataset contains real-world salary information collected from multiple companies across different locations, job roles, and employment types. It is designed to help understand salary trends, pay distribution, and factors influencing compensation in the job market.

The dataset consists of 22,000+ records, making it suitable for exploratory data analysis (EDA), data cleaning, visualization, and business insights generation using Python.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analysis will be helpful for those working in Jobs & Career domain.

Using this dataset, we answered multiple questions with Python in our Project.

Q1: Which Job Roles have Highest Average Salary?

Q2: Which Cities Offer Highest Average Salary?

Q3: Name those 5 companies located in 'New Delhi' with Ratings of '5', offering highest & lowest salaries.

Q4: Which Job Title has the highest number of salary reported?

Q5: Which 10 Companies provide the highest average salary, when at least 20 employees have reported their salaries?

Q.6: Check and show the relationship Between Ratings & Salaries.

Q.7: Does employment status affect salary?

Q.8: Which job roles are most common?

Q.9: How does average salary change as company rating increases?

At Last : A Mini Dashboard

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These are the main Features/Columns available in the dataset :

1) Company Name : The name of the organization offering the job.

2) Job Roles : The broader role category or functional role related to the job (e.g., Web, Android).

3) Location : The city where the job is based.

4) Salary : The annual salary offered for the role (numerical value).

5) Rating : The company’s average employee rating (usually on a scale of 1 to 5).

6) Employment Status : The type of job role such as Full Time, Intern, Contractor, or Trainee.

7) Salaries Reported : The number of employees who reported their salary for that specific role and company.

8) Job Title : The specific designation or position (e.g., Data Analyst, Data Scientist, Software Engineer).

The median income³ of full-time employees⁴ in manager level positions in Switzerland was ******* Swiss francs gross in 2023. Therefore, this was the best-earning occupational group in Switzerland, followed by academic professions, with a median income of ******* Swiss francs. The lowest earning professions, apart from apprentices, were jobs in unskilled labor, with median gross earnings of ******.

This file contains detailed information about data professionals, including their salaries, designations, departments, and more. The data can be used for salary prediction, trend analysis, and HR analytics.

Column Descriptors

FIRST NAME: First name of the data professional (String)

LAST NAME: Last name of the data professional (String)

SEX: Gender of the data professional (String: 'F' for Female, 'M' for Male)

DOJ (Date of Joining): The date when the data professional joined the company (Date in MM/DD/YYYY format)

CURRENT DATE: The current date or the snapshot date of the data (Date in MM/DD/YYYY format)

DESIGNATION: The job role or designation of the data professional (String: e.g., Analyst, Senior Analyst, Manager)

AGE: Age of the data professional (Integer)

SALARY: Annual salary of the data professional (Float)

UNIT: Business unit or department the data professional works in (String: e.g., IT, Finance, Marketing)

LEAVES USED: Number of leaves used by the data professional (Integer)

LEAVES REMAINING: Number of leaves remaining for the data professional (Integer)

RATINGS: Performance ratings of the data professional (Float)

PAST EXP: Past work experience in years before joining the current company (Float)

Provenance

Data Collection:

• The dataset was compiled from internal HR records of a hypothetical company.
• Each record represents a unique data professional with various attributes collected from their employment history.
• The data spans from 2009 to 2016, capturing a snapshot as of January 7, 2016.

Data Organization:

• The data has been organized chronologically by the date of joining (DOJ).
• Each row represents an individual data professional.
• Various attributes such as designation, department, and performance ratings have been included to enable comprehensive analysis.

This dataset contains the updated 2024 data from the Jobs and Salaries in Data Science dataset. The information is sourced from ai-jobs.net/salaries/2024/.

About Dataset

work_year: The year in which the data was recorded. This field indicates the temporal context of the data, important for understanding salary trends over time.

job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.

job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.

salary_currency: The currency in which the salary is paid, such as USD, EUR, etc. This is important for currency conversion and understanding the actual value of the salary in a global context.

salary: The annual gross salary of the role in the local currency. This raw salary figure is key for direct regional salary comparisons.

salary_in_usd: The annual gross salary converted to United States Dollars (USD). This uniform currency conversion aids in global salary comparisons and analyses.

employee_residence: The country of residence of the employee. This data point can be used to explore geographical salary differences and cost-of-living variations.

experience_level: Classifies the professional experience level of the employee. Common categories might include 'Entry-level', 'Mid-level', 'Senior', and 'Executive', providing insight into how experience influences salary in data-related roles.

employment_type: Specifies the type of employment, such as 'Full-time', 'Part-time', 'Contract', etc. This helps in analyzing how different employment arrangements affect salary structures.

work_setting: The work setting or environment, like 'Remote', 'In-person', or 'Hybrid'. This column reflects the impact of work settings on salary levels in the data industry.

company_location: The country where the company is located. It helps in analyzing how the location of the company affects salary structures.

company_size: The size of the employer company, often categorized into small (S), medium (M), and large (L) sizes. This allows for analysis of how company size influences salary.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, gender, and age group.

Annual estimates of paid hours worked and earnings for UK employees by sex, and full-time and part-time, by two-digit Standard Occupational Classification.

This map shows the predominant median earnings for all service occupations: Healthcare Support Occupations, Protective Service Occupations, Food Preparation and Serving Related Occupations, Building and Grounds Cleaning and Maintenance Occupations, and Personal Care and Service Occupations - 2018 Standard Occupation Classification (SOC) codes 31-0000, 33-0000, 35-0000, 37-0000, 39-0000 respectively. Only full-time, year-round workers included (those 16+ who usually work 35+ hrs/week for 50+ weeks/yr). Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). The data is from the U.S. Census Bureau's American Community Survey (ACS). The figures in this map are updated annually when the newest estimates are released by ACS and updated in ArcGIS Living Atlas. For more detailed metadata, click on one of the layers listed below. This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of the ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

In 2024, with the exception of the professional category of armed forces, police and military firefighters, women's earnings in Brazil were always lower than men's. In 2024, a male manager earned an average of ****** Brazilian reals per month, while a female manager earned an average of about ***** reals per month.