This dataset provides comprehensive real-time data from Glassdoor. It includes detailed company information, employee reviews, salary data, interview data, and more for employers worldwide. The data covers company attributes like ratings, reviews, salaries, benefits, and workplace culture details. Users can leverage this dataset for employer research, job market analysis, and workplace intelligence. The API enables real-time access to Glassdoor's vast employer database and review data, helping businesses make data-driven decisions about recruitment, employer branding, and workplace culture. Whether you're conducting market analysis, tracking employer reputation, or building HR tools, this dataset provides current and reliable Glassdoor data. The dataset is delivered in a JSON format via REST API.

InfoTrie’s LinkedIn Data APIs provide a comprehensive solution for talent acquisition, workforce analysis, and competitive benchmarking. Our package includes enriched employee, company, and job data, refreshed daily or as per your schedule, enabling actionable insights for HR and recruitment strategies.

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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.

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4. Integration & Customization: Seamlessly integrate job posting data with your systems via APIs, SFTP, and custom workflows.
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Table of INEBase Salary rates by sex and branch of activity. Quarterly. National. Economically Active Population Survey

Analysis of ‘Average, median, modal annual salary, part time and full time by period. MYH (API identifier: 10882)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-452-10882 on 08 January 2022.

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

Table of INEBase Average, median, modal annual salary, part time and full time by period. National. Women and Men in Spain

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

Costs to the City for salary and benefits listed by job title. Costs to the City for salary and benefits listed by job title. Data contains historical information from year 2014 to Present

Salary information for all mayoral appointees.

The dataset contains the name, gender, county or city of residence (when possible), official title, total salary received during each fiscal year, base salary for the employee, and traveling and subsistence expense reimbursed to state personnel beginning with Fiscal Year 2007.

A status of "TERMINATED" in the column providing the base salary does not indicate that the employee was fired, only that the person no longer works in that position.

Content

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year.

NOTE: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Dean Rose on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Analysis of ‘Gender salary gap (not adjusted to individual characteristics) by hourly salary by age and period Spain. MYH (API identifier: 10888)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-452-10888 on 08 January 2022.

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

Table of INEBase Gender salary gap (not adjusted to individual characteristics) by hourly salary by age and period Spain. Annual. National. Women and Men in Spain

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

Analysis of ‘Gender salary gap (not adjusted to individual characteristics) by hourly salary by sectors of economic activity and period in the EU. MYH (API identifier: 10895)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-452-10895 on 11 January 2022.

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

Table of INEBase Gender salary gap (not adjusted to individual characteristics) by hourly salary by sectors of economic activity and period in the EU. Annual. National. Women and Men in Spain

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

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. By leveraging advanced web scraping technology, this dataset delivers access to job market trends, salary insights, and in-demand skills. A valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence, this data helps businesses stay ahead in a dynamic job market.

Key Features:

✅ 206M+ Job Postings Tracked – Data sourced from 1.8M+ company websites worldwide. ✅ 7M+ Active Job Openings – Continuously updated to reflect real hiring demand. ✅ Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅ Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅ Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅ Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes in the Dataset:

General Information: - id (UUID) – Unique identifier for the job posting. - type (constant: "job_opening") – Object type. - title (string) – Job title. - description (string) – Full job description extracted from the job listing. - url (URL) – Direct link to the job posting. - first_seen_at (ISO 8601 date-time) – When the job was first detected. - last_seen_at (ISO 8601 date-time) – When the job was last observed. - last_processed_at (ISO 8601 date-time) – When the job data was last updated.

Job Metadata:

• contract_types (array of strings) – Employment type (full-time, part-time, contract).
• categories (array of strings) – Job industry categories (engineering, marketing, finance).
• seniority (string) – Seniority level (manager, non_manager).
• status (string) – Job status (open, closed).
• language (string) – Language of the job posting.

Location Data:

• location (string) – Full location details from the job description.
• location_data (array of objects) – Structured location details: -- city (string, nullable) – City where the job is located. -- state (string, nullable) – State or region. -- zip_code (string, nullable) – Postal/ZIP code. -- country (string, nullable) – Country. -- region (string, nullable) – Broader geographical region. -- continent (string, nullable) – Continent name. -- fuzzy_match (boolean) – Indicates if the location was inferred.

Salary Data:

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Salary currency (USD, EUR, GBP).
• salary_low_usd (float, nullable) – Minimum salary converted to USD.
• salary_high_usd (float, nullable) – Maximum salary converted to USD.
• salary_time_unit (string, nullable) – Time unit (year, month, hour).

Occupational Data (ONET):

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (Computer and Mathematical).
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (Python, JavaScript, AI).

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

Response Example: https://docs.predictleads.com/v3/api_endpoints/job_openings_dataset/retrieve_company_s_job_openings

Median pay for women employed by the region as a percentage of median pay for men employed by the region. All salary reported is calculated as full-time salary, SEK/month. Full-time salary includes basic salary plus variable allowances and benefits. For employees aged 18-66 (until 2013 18-64 years) who are monthly or hourly paid. Employees of municipal-owned companies are not included.

The County of Suffolk Annual Salaries File for the Year 2020 is a yearly summary of Payroll Data for all Suffolk County employees. This file contains the Employee Names and Hired Date along with their most recent Job Title and Department. In addition, the file contains the Employee’s Regular Pay Rate (Hourly, Biweekly or Annual Salary), the Year to Date Regular Earnings, Longevity Pay, Overtime Pay, and Other Payments (comprised of Holiday Pay, Night Differential Pay, Cleaning and Clothing Allowances, Taxable Legal Benefit Amounts, etc.). If an employee has been terminated or has separated from County employment, the Separation Payment Amount (if applicable), and Termination Date is also included.

This dataset contains UAE Public Expenditure.Data from Federal Competitiveness and Statistics Authority. Follow datasource.kapsarc.org for timely data to advance energy economics research.2020 Preliminary Data

United Kingdom TE: RC: sa: API: CE: Wages & Salaries data was reported at 212,181.000 GBP mn in Mar 2018. This records an increase from the previous number of 209,549.000 GBP mn for Dec 2017. United Kingdom TE: RC: sa: API: CE: Wages & Salaries data is updated quarterly, averaging 46,718.000 GBP mn from Mar 1955 (Median) to Mar 2018, with 253 observations. The data reached an all-time high of 212,181.000 GBP mn in Mar 2018 and a record low of 2,561.000 GBP mn in Mar 1955. United Kingdom TE: RC: sa: API: CE: Wages & Salaries data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s UK – Table UK.AB023: ESA10: Resources and Uses: Total Economy: Primary Income.

• JobsPikr's USA jobs posting data covers multiple attributes such as - date of job posting, job title, company name, website, salary range, remote or on-the-job, area, etc.
• Flexible data delivery options
• Our delivery frequency is the industry's fastest
• We have historical coverage from 2019
• Our vast database offers a diverse range of sources like ‘Indeed’, ‘Glassdoor’, ‘LinkedIn’ etc. and also source data from company websites
• We cover major geographies

JobsPikr offers Instant access to millions of job posting data records. Use an API to get relevant data records from our database in structured format whenever needed. Get information about targeted jobs for your job board. Analyze data points like HTML job descriptions, localization of job titles, keywords and application URLs that are unique in nature. Jobspikr offers advanced data filtering by domain, experience, salary, and skills, alongside real-time metrics and dashboards for agile HR responsiveness to business demands.

Analysis of ‘Average wages of the main job by period, type of working day, type of contract or employment relationship and decile. EPA (API identifier: 13941)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-348-13941 on 08 January 2022.

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

Table of INEBase Average wages of the main job by period, type of working day, type of contract or employment relationship and decile. Annual. National. Economically Active Population Survey

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

This data on salaries of City of Salinas employees is provided by Transparent California, a service provided by the Nevada Policy Research Institute, a non-profit think tank. For more information on Transparent California, please visit this link.