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

Overview This dataset provides insights into salary distributions across various job classifications, enabling a deeper understanding of compensation trends across industries, experience levels, and geographical locations. It serves as a valuable resource for HR professionals, job seekers, researchers, and policymakers aiming to analyze pay scales, wage gaps, and salary progression trends.

Data Sources The data is aggregated from multiple employment and compensation reports, salary surveys, and publicly available job postings. It has been cleaned, standardized, and structured to ensure consistency and usability for analytical purposes.

Features Job Title: Specific title of the job (e.g., Data Analyst, Software Engineer, Marketing Manager).

Job Classification: Broad category of jobs (e.g., IT, Finance, Healthcare, Education).

Industry: The sector in which the job belongs (e.g., Technology, Banking, Retail).

Experience Level: Categorized as Entry-level, Mid-level, or Senior-level.

Education Requirement: Minimum qualification required for the job role.

Average Salary (INR/USD/Other Currency): The median or mean salary for a particular job classification.

Salary Range: The minimum and maximum salary offered for a role.

Location: Country or region where the job is based.

Employment Type: Full-time, Part-time, Contract, or Remote.

Company Size: Small, Medium, or Large enterprises.

Potential Use Cases Salary Benchmarking: Compare salary expectations across industries and job roles.

Career Planning: Identify lucrative career paths based on salary trends.

Wage Gap Analysis: Examine salary disparities by gender, location, or experience level.

Cost of Living Adjustments: Assess salaries relative to regional economic conditions.

HR and Recruitment Strategies: Optimize compensation packages to attract top talent.

Acknowledgments The dataset is compiled from various salary reports and job market research sources. Special thanks to contributors and organizations providing employment data for analysis.

License This dataset is shared for educational, research, and analytical purposes. Please ensure compliance with relevant data usage policies before any commercial applications.

Get Started The dataset can be explored using Python (Pandas), R, SQL, or visualization tools like Tableau and Power BI. Sample notebooks and analyses are available in the Kaggle notebook section.

As of 2023, the median wage for employees in healthcare support occupations was about 36,140 U.S. dollars. The occupational group with the highest annual median wage was management occupations. Mean wages for the same occupational groups can be accessed here.

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.

The wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

In 2024, people working in IT management in the United States, earned an average annual salary worth around 168 thousand U.S. dollars. Software developers and project managers all reported being paid on average over 120 thousand U.S. dollars. Despite nearly all categories saw a year-on-year increase in annual compensation, IT support and help desk technicians saw a decrease compared to the previous year

Graph and download economic data for 3-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Movement: Job Stayer (FRBATLWGT3MMAUMHWGJMJST) from Mar 1997 to May 2025 about growth, moving average, jobs, 3-month, average, wages, median, and USA.

In 2023, the average wage and salary per full-time equivalent employee in the mining industry in the United States was at 126,707 U.S. dollars. The highest wage and salary per FTE was found in the information industry, at 164,400 U.S. dollars.

This Dataset containes the details of the AI, ML, Data Science Salary (2020- 2025). Salary data is in USD and recalculated at its average fx rate during the year for salaries entered in other currencies.

The data is processed and updated on a weekly basis so the rankings may change over time during the year.

Attribute Information

• work_year: The year the salary was paid.
• experience_level: The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director
• employment_type: The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance
• job_title: The role worked in during the year.
• salary: The total gross salary amount paid.
• salary_currency: The currency of the salary paid as an ISO 4217 currency code.
• salary_in_usd: The salary in USD (FX rate divided by avg. USD rate of respective year) via statistical data from the BIS and central banks.
• employee_residence: Employee's primary country of residence in during the work year as an ISO 3166 country code.
• remote_ratio : The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote/hybird 100 Fully remote (more than 80%)
• company_location: The country of the employer's main office or contracting branch as an ISO 3166 country code.
• company_size: The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Acknowledgements

https://aijobs.net/

Photo by Anastassia Anufrieva on Unsplash

Brazil Average Real Income: All Jobs: Actual Earnings data was reported at 2,304.000 BRL in Mar 2019. This records a decrease from the previous number of 2,531.000 BRL for Feb 2019. Brazil Average Real Income: All Jobs: Actual Earnings data is updated monthly, averaging 2,269.000 BRL from Feb 2012 (Median) to Mar 2019, with 86 observations. The data reached an all-time high of 2,611.000 BRL in Jan 2019 and a record low of 2,147.000 BRL in Apr 2012. Brazil Average Real Income: All Jobs: Actual Earnings data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA001: Continuous National Household Sample Survey: Monthly.

Graph and download economic data for 12-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Stayer (FRBATLWGT12MMUMHWGJST) from Dec 1997 to May 2025 about growth, moving average, 1-year, jobs, average, wages, median, and USA.

A structured overview of the average, net, median, and minimum wage in Germany for 2025. This dataset combines original market research conducted by KUMMUNI GmbH with publicly available data from the German Federal Statistical Office. It includes values with and without bonuses, hourly minimum wage, and take-home pay after tax.

Graph and download economic data for 12-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Switcher (FRBATLWGT12MMUMHWGJSW) from Dec 1997 to May 2025 about growth, moving average, 1-year, jobs, average, wages, median, and USA.

Wages in the United States increased 4.72 percent in May of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The graph presents the median monthly salary in the United States from 2000 to 2024. The x-axis represents the years, labeled from '00 to '24*, while the y-axis shows the salary amounts in U.S. dollars per month. Throughout this twenty-four-year period, the median monthly salary consistently increased from $2,500 in 2000 to $5,036 in 2024. The data highlights a steady upward trend, with annual salaries rising each year without any declines. Notably, the salary grew by approximately $200 each year from 2000 to 2019, surged to $4,269 in 2020, and continued to climb each subsequent year, reaching $5,000 by 2024. This consistent growth reflects economic advancements and potential increases in workforce compensation over the decade. The information is depicted in a line graph format, effectively illustrating the continuous rise in median monthly salaries across the specified years.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Job printers occupations: 16 years and over: Men (LEU0254676500A) from 2000 to 2010 about second quartile, occupation, jobs, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Explore the progression of average salaries for graduates in Job Offered Or Related from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Job Offered Or Related relative to other fields. This data is essential for students assessing the return on investment of their education in Job Offered Or Related, providing a clear picture of financial prospects post-graduation.

Analysis of ‘Average Salary by Job Classification’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c279deaa-d913-48fe-8693-5899e9291025 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

Explore the progression of average salaries for graduates in Technology See Section K, Job 3 from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Technology See Section K, Job 3 relative to other fields. This data is essential for students assessing the return on investment of their education in Technology See Section K, Job 3, providing a clear picture of financial prospects post-graduation.

Economically Active Population Survey: Average wages of the main job by period, type of working day, underemployment and decile. Annual. National.