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.

In 2018, the average annual gross salary of plant managers in Italy amounted to 91.8 thousand euros. The graph, based on data provided by JobPricing, offers a general overview of the annual gross salaries in the manufacturing sector. It includes the salary figures for selected job titles across different grading levels.

A marketing director's average salary was expected to be over ************ Indian rupees per annum in 2024. A pilot and software architect's average salary was estimated to be over ************* rupees. High-paying jobs usually stem from demand for niche skills, technological advancements, and revenue generation for a company, among other factors.

A comprehensive dataset of top job titles for H-1B Visa sponsorships in 2025, including salary data, petition trends, and employer insights. Updated annually with the latest trends and employer behavior regarding H-1B visa sponsorship.

Explore the progression of average salaries for graduates in Educational Human Re*** (See Job 1 For Full Title) 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 Educational Human Re*** (See Job 1 For Full Title) relative to other fields. This data is essential for students assessing the return on investment of their education in Educational Human Re*** (See Job 1 For Full Title), providing a clear picture of financial prospects post-graduation.

The statistic gives the results of the annual salary survey among logistics and supply chain professionals, asking respondents about their annual salaries including bonuses and other compensations in 2016 and 2017, and broken down by job function. In that period, the average salary for a supply chain management employee amounted to about 120,175 U.S. dollars, down from 141,540 U.S. dollars in the previous year.

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.

Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:

• Deduplication Model: Filters out exact and near-duplicates, ensuring unique, high-quality job data.
• Title Taxonomy Model: Categorizes over 20 million titles into 50,000 standardized groups, simplifying analysis.
• Skill & Qualification Models: Extracts hard skills, soft skills, certifications, and degrees, mapped to standard education levels and tailored by job context.
• Job Category Model: Predicts work types (remote, onsite, hybrid), seniority, and employment types (full-time, contract).
• Location Prediction Model: Parses or estimates job locations—ZIP code, city, state—using averaged lat/long values from search-based estimates for accuracy.
• Salary Estimation Model: Predicts minimum, average, and maximum salaries, using parsed and AI-predicted values for a robust salary range.
• Government Classification Models: Assigns SOC codes and titles to postings for structured role insights and regulatory compliance.
• Human Feedback Model: An in-house team of annotators reviews job descriptions and AI outputs, refining model accuracy.

Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.

Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.

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.

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

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

Average hourly wages for women and men in all represented and non-represented step-progression job classes

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.

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.

Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:

• Deduplication Model: Filters out exact and near-duplicates, ensuring unique, high-quality job data.
• Title Taxonomy Model: Categorizes over 20 million titles into 50,000 standardized groups, simplifying analysis.
• Skill & Qualification Models: Extracts hard skills, soft skills, certifications, and degrees, mapped to standard education levels and tailored by job context.
• Job Category Model: Predicts work types (remote, onsite, hybrid), seniority, and employment types (full-time, contract).
• Location Prediction Model: Parses or estimates job locations—ZIP code, city, state—using averaged lat/long values from search-based estimates for accuracy.
• Salary Estimation Model: Predicts minimum, average, and maximum salaries, using parsed and AI-predicted values for a robust salary range.
• Government Classification Models: Assigns SOC codes and titles to postings for structured role insights and regulatory compliance.
• Human Feedback Model: An in-house team of annotators reviews job descriptions and AI outputs, refining model accuracy.

Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.

Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.

This data will be updated when further major updates occur, either as a result of collective bargaining or updates to the City’s overall salary structure resulting from the Job Architecture System Project.

This dataset is a listing of all active City of Edmonton job code titles and salary ranges. The working title may be different.

Data Owner: Compensation and Classification, Employee Services

The statistic shows the median salaries of employees in local television news in the United States in 2017, sorted by job title. According to the source, local TV news anchors earned an average of 70 thousand U.S. dollars annually as of 2017.

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.

In 2018, around 63 percent of women were offered, on average, lower salaries than men for the same job title at the same company in the technology industry. Three years later, in 2021, the share of jobs offering lower wages dropped to 61.8 percent.

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

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.

Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:

• Deduplication Model: Filters out exact and near-duplicates, ensuring unique, high-quality job data.
• Title Taxonomy Model: Categorizes over 20 million titles into 50,000 standardized groups, simplifying analysis.
• Skill & Qualification Models: Extracts hard skills, soft skills, certifications, and degrees, mapped to standard education levels and tailored by job context.
• Job Category Model: Predicts work types (remote, onsite, hybrid), seniority, and employment types (full-time, contract).
• Location Prediction Model: Parses or estimates job locations—ZIP code, city, state—using averaged lat/long values from search-based estimates for accuracy.
• Salary Estimation Model: Predicts minimum, average, and maximum salaries, using parsed and AI-predicted values for a robust salary range.
• Government Classification Models: Assigns SOC codes and titles to postings for structured role insights and regulatory compliance.
• Human Feedback Model: An in-house team of annotators reviews job descriptions and AI outputs, refining model accuracy.

Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.

Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.