This dataset provides comprehensive real-time data from Glassdoor. It includes detailed company information, employee reviews, job postings, 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.

The OpenWeb Ninja Glassdoor Data API provides real-time access to extensive company data and employer reviews data from Glassdoor.

Key company data points included in the dataset: Name, Rating, Website, Salary and Job counts, Company size, Revenue, Stock, Competitors, Awards won, and 30+ more data points.

Key employer review data points included in the dataset: Review summary, Pros / Cons, Employee status, Location, Work-Life balance, CEO rating, and 20+ more data points.

OpenWeb Ninja's Glassdoor Data API Stats & Capabilities: - 2M+ Companies/Employers - 80M+ Employee Reviews - 30+ company data points - 20+ review data points - Company search capability

OpenWeb Ninja's Glassdoor Data API common use cases: - Investors and Market Analysts - Market and Industry Trends - Competitive Analysis - Company Insights

Fast and reliable real-time API access to Glassdoor data with 2M+ companies and 80M+ employer reviews from around the world.

➡️ Choose from multiple data formats, delivery frequencies, and delivery methods

➡️ Extensive Salary Datasets with Job Postings data from 5 leading B2B data sources

➡️ 20+ salary-related data points from Glassdoor

➡️ API for effortless search and enrichment (via a user-friendly self-service tool)

➡️ Fresh Salary Data: daily updates, easy change tracking with dedicated data fields

➡️ Our full support for evaluating the Coresignal Salary Database: free consultation, data sample, and free credits for API testing.

✅ For HR tech

Wage Data can provide insights into the demand for different types of jobs and skills, as well as income trends over time. With access to historical compensation data, companies can develop their own predictive models.

✅ For Investors

Explore expansion trends, analyze hiring practices, and predict company or industry growth rates, enabling the extraction of actionable strategic and operational insights. At a larger scale of analysis, Job Postings Data can be leveraged to forecast market trends and predict the growth of specific industries.

✅ For Lead generation

Coresignal’s Job Postings Data is ideal for lead generation and determining purchasing intent. In B2B sales, job postings can help identify the best time to approach a prospective client.

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016)
2. Always fresh data, updated daily, weekly, or monthly
3. Exceptional client service
4. Responsible and secure data collection

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:

✅ 232M+ Job Postings Tracked – Data sourced from 92 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.

PredictLeads Job Openings Docs https://docs.predictleads.com/v3/guide/job_openings_dataset

This Google for Jobs API dataset provides comprehensive real-time job listing data directly from Google's job search engine. It includes detailed job information such as titles, descriptions, requirements, salaries, locations, employer details, and application links. The data aggregates listings from major job boards, company websites, and recruiting platforms that appear in Google for Jobs search results. Users can leverage this API for building job search applications, conducting employment market research, salary analysis, and career development tools. The API supports advanced filtering by location, job type, experience level, salary range, and company size. Whether you're developing a recruitment platform, job board, or workforce analytics tool, this Google for Jobs API provides current and reliable employment data directly from Google's comprehensive job search index.

Salary information for all mayoral appointees.

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.

Average salary data among 671 cities across the world. Data were taken from Numbeo as an aggregation of user voting. For some cities, data start from 2011 or 2012 year. In this case, all year columns without data have 0 value.

This dataset is one of the public parts of City API project data. Need more? Try our full data

Salary Clean Aleyna Yildirim Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that includes salary and compensation data. The dataset consists of salary data by employee reported by Local Authorities that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Zhanjiang Laiyixin Salary Supply Chain Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Data covering the period from September 18, 2023, to October 17, 2023. Regions: across all of Russia.

What can you explore using this data?

1) You can conduct a comparative analysis of the offered salaries in the IT industry: Tasks related to this group will focus on analyzing IT salaries based on various criteria. Examples of charts that can be created: * Median salaries depending on the city * Median salaries depending on the professional role * Median salaries depending on the type of employment

2) Examination of the distribution of required experience from applicants and analysis of remuneration depending on experience: * Distribution of required work experience in the IT industry * Distribution of required experience depending on the work schedule * Dependence of salary on work experience.

3) Determination of the top employers: Tasks related to this group are aimed at determining companies most actively posting vacancies in the IT industry and analyzing their personnel needs, key skills, and work experience. Examples of charts: * Distribution of work experience in large companies. * Distribution of programming languages in large companies.

4) Determination of the most sought-after skills for the profession of a developer programmer

5) Since the coordinates of the workplace are specified in the job listing, the data can be transferred to a geographical map and a heat map of vacancies can be created.

6) Forecasting salary based on experience and skills: You can build a model that predicts salary based on given parameters, such as experience and skills.

Payroll API Market Outlook

According to our latest research, the global Payroll API market size reached USD 1.42 billion in 2024, reflecting a dynamic landscape driven by digital transformation and increasing automation in payroll management. The market is expected to grow at a CAGR of 12.8% during the forecast period, reaching approximately USD 4.23 billion by 2033. This robust expansion is propelled by the rising demand for seamless payroll integration, regulatory compliance, and the proliferation of cloud-based payroll solutions across diverse industry verticals.

One of the primary growth factors in the Payroll API market is the accelerating shift toward digital payroll management systems, especially among small and medium enterprises (SMEs) seeking operational efficiency. Traditional payroll processes are often marred by manual errors, compliance risks, and time-consuming administrative tasks. Payroll APIs provide a streamlined, automated approach to payroll processing, reducing errors and enhancing accuracy. As businesses increasingly prioritize employee experience and compliance, the demand for integrated payroll solutions continues to surge. The ability of Payroll APIs to connect disparate HR, finance, and accounting systems is a significant driver, enabling organizations to maintain real-time data synchronization and accurate payroll computation.

Another significant growth driver is the evolving regulatory landscape, which necessitates strict adherence to labor laws, tax regulations, and data privacy standards. Payroll APIs play a crucial role in ensuring organizations remain compliant with local, regional, and international regulations, thereby reducing the risk of penalties and legal disputes. As governments across the globe introduce new compliance mandates and reporting standards, businesses are compelled to adopt advanced payroll solutions that can adapt swiftly to regulatory changes. This has spurred investments in Payroll API platforms that offer real-time updates, automated tax calculations, and comprehensive audit trails, making compliance management more efficient and less resource-intensive.

The proliferation of cloud-based technologies and the rise of remote and hybrid work models have further fueled the adoption of Payroll API solutions. Cloud deployment offers scalability, flexibility, and accessibility, making it an attractive option for organizations with geographically dispersed workforces. Payroll APIs enable seamless integration with other cloud-based HR and financial applications, facilitating end-to-end automation of payroll processes. Additionally, the growing focus on data security and privacy has led to the development of secure Payroll API solutions that ensure sensitive employee information is protected throughout the payroll lifecycle. These technological advancements are expected to continue driving market growth over the next decade.

Regionally, North America remains the dominant market for Payroll API solutions, accounting for the largest revenue share in 2024, thanks to the presence of leading technology providers and the early adoption of digital payroll systems. Europe follows closely, driven by stringent regulatory requirements and a strong emphasis on employee rights and data protection. The Asia Pacific region is experiencing the fastest growth, propelled by rapid digitalization, expanding SME sectors, and increasing awareness about payroll automation benefits. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as organizations in these regions begin to recognize the value of Payroll API integration in modernizing their HR operations.

The concept of Embedded Payroll Accounts is gaining traction as organizations look to streamline payroll operations and enhance financial management. By integrating payroll directly with banking services, businesses can offer employees a seamless experience, allowing for direct deposits, automated savings, and easy access to financial services. This integration not only simplifies payroll processing but also enhances employee satisfaction by providing them with greater control over their finances. As the demand for integrated financial solutions grows, the adoption of Embedded Payroll Accounts is expected to rise, offering a competitive edge to businesses that prioritize employee financial wellness.

City-Parish employees' annual salaries and other payroll related information. Information is calculated after the last payroll is run for the year specified. Some fields, such as job title and department, are accurate as of the time the data was captured for Open Data BR. For example, if an employee worked for three departments throughout the year, only the department they worked for at the time we collected the data will be shown.

*In November of 2018, the City-Parish switched to a new payroll system. This data contains employee information from 2018 onward. For prior year data, please see the Legacy City-Parish Employee Annual Salaries https://data.brla.gov/Government/Legacy-City-Parish-Employee-Annual-Salaries/g5c2-myyj

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Employee payroll data for all Cook County employees excluding Forest Preserves, indicating amount of base salary paid to an employee during the County fiscal quarter. Salaries are paid to employees on a bi-weekly basis.

Any pay period that extended between quarters will be reported to the quarter of the Pay Period End Date. (e.g. If a Pay Period runs 02/21-03/05, that pay period would be reported in the Q2 period, as the end of the pay period falls in March - Q2)

The county fiscal quarters are:

Q1: December - February

Q2: March - May

Q3: June - August

Q4: September - November

The Employee Unique Identifier field is a unique number assigned to each employee for the purpose of this data set, that is not their internal employee ID number, and allows an employee to be identified in the data set over time, in case of a name change or other change. This number will be consistent within the data set, but we reserve the right to regenerate this number over time across the data set.

ISSUE RESOLVED: As of 4/19/2018 there was an issue regarding employee FY2016 and FY2017 payroll in which records were duplicated in the quarterly aggregation, resulting in inflated base pay amounts. Please disregard any data extracted from this dataset prior to the correction date and use this version moving forward.

KNOWN ISSUE: Several records are missing Bureau and Office information. We are working on correcting this and will update the dataset when this issue has been resolved.

For data prior to Fiscal Year 2016, see datasets at https://datacatalog.cookcountyil.gov/browse?tags=payroll

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Income Verification via Payroll APIs Market Outlook

According to our latest research, the income verification via payroll APIs market size reached USD 2.1 billion globally in 2024. The market is experiencing a robust growth trajectory with a recorded compound annual growth rate (CAGR) of 19.8% from 2025 to 2033. By the end of 2033, the global income verification via payroll APIs market is projected to attain a value of USD 10.3 billion, driven by the increasing demand for real-time, accurate, and secure income verification solutions across financial, employment, and government sectors. The surge in digital transformation initiatives and the adoption of open banking standards are key growth factors propelling this market forward, as per our comprehensive industry analysis.

The primary growth driver for the income verification via payroll APIs market is the escalating need for instant, reliable, and automated income verification processes in lending and financial services. Traditional income verification methods are time-consuming, error-prone, and often susceptible to fraud. Payroll APIs revolutionize this space by enabling seamless, real-time connectivity between payroll systems and financial institutions, thereby reducing manual intervention and operational costs. This technological advancement not only accelerates loan approvals and credit decisions but also enhances customer experience by minimizing wait times and paperwork. As digital onboarding becomes the norm, especially in banking and fintech, the reliance on payroll APIs for secure income data is expected to intensify, further fueling market expansion.

Another significant growth factor is the regulatory push towards data privacy, transparency, and open finance, particularly in mature markets like North America and Europe. Regulatory bodies are encouraging the adoption of standardized APIs to facilitate secure data sharing between employers, payroll providers, and third-party verifiers. This regulatory backing has led to the proliferation of API-based solutions that comply with stringent security and privacy mandates, such as GDPR and CCPA. Moreover, the rise of gig economy workers and freelancers, whose income streams are often non-traditional and variable, has made conventional verification methods obsolete. Payroll APIs offer a scalable and adaptable solution, providing accurate income data for diverse employment scenarios, which is crucial for lenders, landlords, and government agencies assessing eligibility or risk.

Technological innovation is also contributing remarkably to the income verification via payroll APIs market’s growth. The integration of artificial intelligence, machine learning, and advanced analytics with payroll APIs is enhancing the accuracy and predictive capabilities of income verification systems. These technologies enable real-time anomaly detection, fraud prevention, and dynamic risk assessment, which are invaluable for financial institutions and employers. Furthermore, the increasing partnerships between fintech companies, payroll providers, and traditional banks are expanding the reach and utility of payroll APIs. As more organizations recognize the operational efficiencies and risk mitigation benefits, the adoption curve is expected to steepen, especially in emerging markets that are rapidly digitizing their financial infrastructure.

Regionally, North America currently dominates the income verification via payroll APIs market, accounting for the largest share in 2024. The region’s leadership is attributed to its advanced digital infrastructure, early adoption of open banking standards, and a highly competitive fintech ecosystem. Europe follows closely, driven by robust regulatory frameworks and a growing demand for cross-border income verification solutions. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digitization, rising financial inclusion initiatives, and increasing investments in API-driven platforms. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as digital transformation accelerates across their financial and employment sectors.

Table of INEBase Salary rates by sex and branch of activity. Quarterly. National. Economically Active Population Survey

The City’s compensation disclosure list includes position titles and base salary ranges as approved by Council from 2014 to present.

For more information see: Compensation disclosure list

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

United Kingdom TE: RC: API: Compensation of Employee (CE) data was reported at 256,106.000 GBP mn in Jun 2018. This records a decrease from the previous number of 268,544.000 GBP mn for Mar 2018. United Kingdom TE: RC: API: Compensation of Employee (CE) data is updated quarterly, averaging 54,009.500 GBP mn from Mar 1955 (Median) to Jun 2018, with 254 observations. The data reached an all-time high of 268,544.000 GBP mn in Mar 2018 and a record low of 2,724.000 GBP mn in Mar 1955. United Kingdom TE: RC: API: Compensation of Employee (CE) data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.AB023: ESA10: Resources and Uses: Total Economy: Primary Income.