Popular US workplace blog AskAManager (askamanager.org) sponsors an annual salary survey of blog readers. The 2023 survey collected data about industry, job function, title, annual salary, additional compensation, race, gender, remote/on-site requirements, education, location, and years' experience.

The dataset here features responses collected between April 11 and 28, 2023, and has some 16,000 responses. This version of the data set has employed several feature engineering techniques to group and cleanse data, convert the currency to USD values as of April 1, 2023, and add clarity to location data. In particular, US respondents were paired when possible with a metropolitan area.

Unlock valuable salary insights with our comprehensive Salary Dataset, designed for businesses, recruiters, and job seekers to analyze compensation trends, workforce planning, and market competitiveness.

Dataset Features

Job Listings & Salaries: Access structured salary data from top job platforms, including job titles, company names, locations, salary ranges, and compensation types. Employer & Industry Insights: Extract company-specific salary trends, industry benchmarks, and hiring patterns. Geographic Pay Disparities: Compare salaries across different regions, cities, and countries to identify location-based compensation trends. Job Market Trends: Monitor salary fluctuations, demand for specific roles, and hiring trends over time.

Customizable Subsets for Specific Needs Our Salary Dataset is fully customizable, allowing you to filter data based on job titles, industries, locations, experience levels, and salary ranges. Whether you need broad market insights or focused data for recruitment strategy, we tailor the dataset to your needs.

Popular Use Cases

Workforce Planning & Talent Acquisition: Optimize hiring strategies by analyzing salary benchmarks and compensation trends. Market Research & Competitive Intelligence: Compare salaries across industries and competitors to stay ahead in talent acquisition. Career Decision-Making: Help job seekers evaluate salary expectations and identify high-paying opportunities. AI & Predictive Analytics: Use structured salary data to train AI models for job market forecasting and compensation analysis. Geographic Expansion & Business Strategy: Assess salary variations across regions to plan business expansions and remote workforce strategies.

Whether you're optimizing recruitment, analyzing salary trends, or making data-driven career decisions, our Salary Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

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.

The project, funded by the National Science Foundation (NSF) GRANTED program, focuses on identifying and addressing the critical needs and challenges faced by the research administrator workforce across institutions and organizations in the United States. The overarching goal is to develop a national job classification system that standardizes job titles, roles, and salary structures for research administrators. This initiative stems from the recognition that research administrators play a pivotal role in supporting faculty and enhancing research productivity. However, inconsistencies in job classifications and compensation have led to high turnover rates and recruitment challenges, particularly exacerbated by the COVID-19 pandemic. The project involved a comprehensive approach, including a virtual focus group, survey, and workshop, to gather diverse perspectives and develop actionable solutions. By creating a standardized system, the project seeks to improve recruitment, retention, and overall workforce effectiveness, contributing to the success and innovation of research enterprises nationwide. The following files are available and shared:1. Survey Instrument - This deposit includes the complete survey instrument used in the NSF-funded project. The survey is designed to collect data on the roles, responsibilities, and challenges faced by research administrators, aiming to gather insights to inform the creation of a national job classification system. The survey was distributed nationally using SurveyMonkey to a broad range of research administrators and was open for participation from June 17 to July 17, 2024. Distribution lists included the Society of Research Administrators International (SRAI) membership, National Council of University Research Administrators (NCURA) membership, NSF GRANTED listserv, Research Administration Listserv (RESADM-L), focus group participants, and the Midwest Research and Graduate Administrators Forum. Administered by SRAI on behalf of Jennifer Woodward and Evan Roberts, the survey remained open for 30 days with one reminder. No compensation was provided for participation. Questions 35 and 36 in the survey were adapted with permission from the CUPA-HR Employee Retention Survey. Sharing this instrument supports transparency and allows others to replicate and build upon the study.2. Survey Data - The survey data deposit contains the raw dataset collected using the survey instrument. The data includes responses from 2,441 research administrators across various institutions, providing a comprehensive view of their experiences and challenges. This dataset is essential for analyzing trends and validating findings that will contribute to the development of a national job classification system. The data are anonymized and shared to foster collaboration in addressing workforce issues and enhancing research administration effectiveness.3. Survey Analysis Slides - The slides summarize the analysis of the survey data. The slides present key findings and insights into the needs and challenges faced by research administrators, including issues related to job title consistency, roles and responsibilities, and salary structures. Visual representations, such as charts and graphs, illustrate the analysis of survey responses, highlighting significant challenges and potential solutions. These slides are shared to communicate the project's outcomes and support informed discussions on improving research administration through standardization and strategic initiatives.

🧠 About the Data

This dataset explores how remote work opportunities intersect with salaries, experience, and employment types across industries. It contains clean, structured records of 500 hypothetical employees in remote or hybrid job roles, suitable for salary modeling, HR analytics, or industry-based salary insights.

📌 Column Descriptions

📈 Potential Use Cases

• Predictive modeling for salary based on role, experience, and location
• Salary benchmarking per industry or employment type
• Visualizing remote vs onsite salary disparities
• Market research for HR and hiring trends
• Exploratory analysis on global employment models

Description: The dataset comprises anonymized data on salaries in the data science field, capturing various aspects such as experience level, employment type, and remote work ratio. This dataset can be utilized for analyzing salary trends in data science, including variations across job titles, locations, and experience levels. It can aid in understanding the impact of factors like remote work, company size, and location on compensation in data science roles. This dataset can be utilized for analyzing compensation patterns and trends in data science roles across various demographics. It can help in benchmarking salaries based on experience, job title, and company size, as well as evaluating the effects of remote work and geographic location on salary levels. Features: Column Name Description work_year The year in which the salary data was collected. experience_level The employee's experience level (e.g., Junior, Mid-level, Senior, Expert). employment_type The type of employment (e.g., Full-Time, Part-Time, Contract). job_title The title or role of the employee in the data science field. salary The employee's salary in the currency specified by salary_currency. salary_currency The currency in which the salary is denoted. salary_in_usd The employee's salary converted to USD for standardization. employee_residence The location of the employee's residence. remote_ratio The percentage of remote work allowed for the position (e.g., 0, 50, 100). company_location The location of the company where the employee works. company_size The size of the company based on employee count (e.g., Small, Medium, Large).

This dataset has been published by the Human Resources Department of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.Distributed bydata.virginiabeach.gov2405 Courthouse Dr.Virginia Beach, VA 23456EntityEmployee SalariesPoint of ContactHuman ResourcesSherri Arnold, Human Resources Business Partner IIIsharnold@vbgov.com757-385-8804Elda Soriano, HRIS Analystesoriano@vbgov.com757-385-8597AttributesColumn: DepartmentDescription: 3-letter department codeColumn: Department DivisionDescription: This is the City Division that the position is assigned to.Column: PCNDescription: Tracking number used to reference each unique position within the City.Column: Position TitleDescription: This is the title of the position (per the City’s pay plan).Column: FLSA Status Description: Represents the position’s status with regards to the Fair Labor Standards Act (FLSA) “Exempt” - These positions do not qualify for overtime compensation – Generally, a position is classified as FLSA exempt if all three of the following criteria are met: 1) Paid at least $47,476 per year ($913 per week); 2) Paid on a salary basis - generally, salary basis is defined as having a guaranteed minimum amount of pay for any work week in which the employee performs any work; 3) Perform exempt job duties - Job duties are split between three classifications: executive, professional, and administrative. All three have specific job functions which, if present in the employee’s regular work, would exempt the individual from FLSA. Employees may also be exempt from overtime compensation if they are a “highly compensated employee” as defined by the FLSA or the position meets the criteria for other enumerated exemptions in the FLSA.“Non-exempt” – These positions are eligible for overtime compensation - positions classified as FLSA non-exempt if they fail to meet any of exempt categories specified in the FLSA. Column: Initial Hire DateDescription: This is the date that the full-time employee first began employment with the City.Column: Date in TitleDescription: This is the date that the full-time employee first began employment in their current position.Column: SalaryDescription: This is the annual salary of the full-time employee or the hourly rate of the part-time employee.Frequency of dataset updateMonthly

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.

This powerful dataset represents a meticulously curated snapshot of the United States job market throughout 2021, sourced directly from CareerBuilder, a venerable employment website founded in 1995 with a formidable global footprint spanning the US, Canada, Europe, and Asia. It offers an unparalleled opportunity for in-depth research and strategic analysis.

Dataset Specifications:

• Source: CareerBuilder.com (US Listings)
• Crawled by: Crawl Feeds in-house team
• Volume: Over 422,000 unique job records
• Timeliness: Last crawled in May 2021, providing a critical historical benchmark for post-pandemic labor market recovery and shifts.
• Format: Compressed ZIP archive containing structured JSON files, designed for seamless integration into databases, analytical platforms, and machine learning pipelines.
• Accessibility: Published and available immediately for acquisition.

Richness of Detail (22 Comprehensive Fields):

The true analytical power of this dataset stems from its 22 granular data points per job listing, offering a multi-faceted view of each employment opportunity:

1. Core Job & Role Information:

   • id: A unique, immutable identifier for each job posting.
   • title: The specific job role (e.g., "Software Engineer," "Marketing Manager").
   • description: A condensed summary of the role, responsibilities, and key requirements.
   • raw_description: The complete, unformatted HTML/text content of the original job posting – invaluable for advanced Natural Language Processing (NLP) and deeper textual analysis.
   • posted_at: The precise date and time the job was published, enabling trend analysis over daily or weekly periods.
   • employment_type: Clarifies the nature of the role (e.g., "Full-time," "Part-time," "Contract," "Temporary").
   • url: The direct link back to the original job posting on CareerBuilder, allowing for contextual validation or deeper exploration.

2. Compensation & Professional Experience:

   • salary: Numeric ranges or discrete values indicating the compensation offered, crucial for salary benchmarking and compensation strategy.
   • experience: Specifies the level of professional experience required (e.g., "Entry-level," "Mid-senior level," "Executive").

3. Organizational & Sector Context:

   • company: The name of the employer, essential for company-specific analysis, competitive intelligence, and brand reputation studies.
   • domain: Categorizes the job within broader industry sectors or functional areas, facilitating industry-specific talent analysis.

4. Skills & Educational Requirements:

   • skills: A rich collection of keywords, phrases, or structured tags representing the specific technical, soft, or industry-specific skills sought by employers. Ideal for identifying skill gaps and emerging skill demands.
   • education: Outlines the minimum or preferred educational qualifications (e.g., "Bachelor's Degree," "Master's Degree," "High School Diploma").

5. Precise Geographic & Location Data:

   • country: Specifies the country (United States for this dataset).
   • region: The state or province where the job is located.
   • locality: The city or town of the job.
   • address: The specific street address of the workplace (if provided), enabling highly localized analysis.
   • location: A more generalized location string often provided by the job board.
   • postalcode: The exact postal code, allowing for granular geographic clustering and demographic overlay.
   • latitude & longitude: Geospatial coordinates for precise mapping, heatmaps, and proximity analysis.

6. Crawling Metadata:

   • crawled_at: The exact timestamp when each individual record was acquired, vital for understanding data freshness and chronological analysis of changes.

Expanded Use Cases & Analytical Applications:

This comprehensive dataset empowers a wide array of research and commercial applications:

• Deep Labor Market Trend Analysis:

  • Identify the most in-demand job titles, skills, and educational backgrounds across different US regions and industries in 2021.
  • Analyze month-over-month or quarter-over-quarter hiring trends to understand recovery patterns or shifts in specific sectors post-pandemic.
  • Spot emerging job roles or skill combinations that gained prominence during the dataset's period.
  • Assess the volume of remote vs. in-person job postings and their distribution.

• Strategic Talent Acquisition & HR Analytics:

  • Benchmark job requirements, salary ranges, and desired experience levels against market averages for specific roles.
  • Optimize job descriptions by identifying common keywords and phrases used by top employers for similar positions.
  • Understand the competitive landscape for talent in specific geographic areas or specialized skill sets.
  • Develop data-driven recruitment strategies by identifying where and how competitors are hiring.

• Compensation & Benefits Research:

  • Conduct detailed salary analysis broken down by job title, industry, location (state, city, even postal code), experience level, and required skills.
  • Identify potential salary premiums or discrepancies for niche skills or hard-to-fill roles.
  • Support robust compensation planning and negotiation strategies.

• Educational & Workforce Development Planning:

  • Universities and vocational schools can align curriculum with real-world employer demand by analyzing required skills and education fields.
  • Government agencies can identify areas for workforce retraining or development programs based on skill gaps revealed in job postings.
  • Career counselors can advise job seekers on in-demand skills and promising career paths.

• Economic Research & Forecasting:

  • Economists can use the volume and nature of job postings as a leading indicator for economic activity and regional growth.
  • Analyze the impact of economic policies or global events on specific industries' hiring patterns.
  • Study labor mobility and migration patterns based on job locations.

• Competitive Intelligence for Businesses:

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Job Postings in Europe

Exploring Salaries, Job Types and Locations

By [source]

About this dataset

The eMedCareers Europe dataset contains more than 30,000 job postings from the top companies across Europe. This comprehensive data set includes detailed information about each job posting such as the associated job category, company name, location, job title and description, as well as types of jobs and salary offered. With this data set, researchers can gain insights into the current trends in salaries and job types in various parts of Europe. Moreover, it provides unique insights into which companies are actively hiring for specific positions and wage levels to assist businesses in forming competitive salaries structures. With this dataset at your fingertips you can start uncovering intriguing patterns in European employment and pay scales - providing deep understandings of the current hiring climate across multiple countries within the region

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How to use the dataset

This dataset contains information on job postings from top companies in Europe. It can be used to analyze job types, salaries and locations of advertised positions. The data fields include: - Category: This field specifies the type or category of the position being advertised, such as Engineering, Marketing or Accounting. - Company Name: This field identifies the company advertising the position. - Job Description: This field provides a short description about what will be expected from an individual in this role. - Job Title: This field displays the title of the role that is being offered. - Job Type: This field specifies full-time, part-time contract work etc which would either be available for direct hire or freelance gigs. - Location: This field denotes where these positions are located in Europe and who could apply for it based on their location/residence. - Salary Offered :This filed provides gross annual salary or pay range that is being offered by employer to employee who takes up this job title and other compensation benefits as part per contract terms and conditions set while signing up for specific roles in company/organization

Using this dataset you can easily analyze all these different aspects related to job openings in Europe available at eMedCareers portal like salary statistics for different industries/categories, job types – full-time vs freelance/contract; location wise jobs availability etc making more informed decision when looking out into market looking out new career opportunities with prospective employers based upon your skillset

Research Ideas

• Analyzing the correlation between salary offered and job type (full-time, part-time, contract) to identify salary trends across different job types in Europe.
• Using the job category and location data to create a geographical analysis of demand for certain roles and skillsets in Europe.
• Tracking changes in the average salaries over time by visualizing posting date vs salary_offered data points

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: emed_careers_eu.csv | Column name | Description | |:--------------------|:-------------------------------------------------------| | category | The job category of the posting. (String) | | company_name | The name of the company posting the job. (String) | | job_description | A description of the job. (String) | | job_title | The title of the job. (String) | | job_type | The type of job (full-time, part-time, etc.). (String) | | location | The location of the job. (String) | | post_date | The date the job was posted. (Date) | | salary_offered | The salary offered for the job. (Integer) |

Acknowledgements

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

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

A comprehensive dataset of top job titles for Green Card sponsorships in 2025, including salary data, petition trends, and employer insights. Updated annually with the latest data on Green Card sponsorship trends and employer behavior.

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

Dataset Card for Data Science Job Salaries

  Dataset Summary





  Content

Column Description

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… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/data-science-job-salaries.

Dataset Overview

This dataset provides a comprehensive collection of job listings from Indeed India, offering valuable insights into the Indian job market. It includes detailed information about job titles, companies, locations, salaries, and job descriptions across various industries and regions.

Key Features

Job Titles and Descriptions: Detailed information about job roles and responsibilities, helping to understand the skills and qualifications required in different industries. Company Information: Data on companies hiring, providing insights into industry demand and company-specific job trends. Location Data: Geographic information about job locations, enabling analysis of regional job markets and opportunities. Salary Information: Salary ranges and compensation details, offering insights into pay scales across different sectors and positions. Industry Coverage: A wide range of industries, from IT and finance to healthcare and manufacturing, providing a holistic view of the job market. Use Cases Market Analysis: Analyze trends in job postings and hiring demand across different sectors and regions. Salary Benchmarking: Understand compensation trends to benchmark salaries for specific roles and industries. Skill Gap Analysis: Identify in-demand skills and qualifications to inform education and training programs. Regional Insights: Explore job availability and demand in different geographic areas to guide relocation or expansion decisions.

For businesses and researchers looking to access more tailored and up-to-date job data, PromptCloud offers custom web scraping services. With expertise in extracting data from a variety of online sources, PromptCloud can provide high-quality, relevant data solutions tailored to your specific needs. Visit PromptCloud to learn more about how their services can empower your data-driven decision-making. https://bit.ly/3Sz9NOH

License This dataset is intended for educational and research purposes only. Please ensure compliance with Indeed's terms of service and applicable laws when using this data.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. Using advanced web scraping technology, our dataset offers real-time access to job trends, salaries, and skills demand, making it a valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence.

Key Features:

✅214M+ Job Postings Tracked – Data sourced from 92 Million company websites worldwide. ✅7,1M+ Active Job Openings – Updated in real-time to reflect 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:

• id (string, UUID) – Unique identifier for the job posting.
• type (string, constant: "job_opening") – Object type.
• title (string) – Job title.
• description (string) – Full job description, extracted from the job listing.
• url (string, URL) – Direct link to the job posting.
• first_seen_at – Timestamp when the job was first detected.
• last_seen_at – Timestamp when the job was last detected.
• last_processed_at – Timestamp when the job data was last processed.

Job Metadata:

• contract_types (array of strings) – Type of employment (e.g., "full time", "part time", "contract").
• categories (array of strings) – Job categories (e.g., "engineering", "marketing").
• seniority (string) – Seniority level of the job (e.g., "manager", "non_manager").
• status (string) – Job status (e.g., "open", "closed").
• language (string) – Language of the job posting.
• location (string) – Full location details as listed in the job description.
• Location Data (location_data) (array of objects)
• city (string, nullable) – City where the job is located.
• state (string, nullable) – State or region of the job location.
• zip_code (string, nullable) – Postal/ZIP code.
• country (string, nullable) – Country where the job is located.
• region (string, nullable) – Broader geographical region.
• continent (string, nullable) – Continent name.
• fuzzy_match (boolean) – Indicates whether the location was inferred.

Salary Data (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) – Currency of the salary (e.g., "USD", "EUR").
• salary_low_usd (float, nullable) – Converted minimum salary in USD.
• salary_high_usd (float, nullable) – Converted maximum salary in USD.
• salary_time_unit (string, nullable) – Time unit for the salary (e.g., "year", "month", "hour").

Occupational Data (onet_data) (object, nullable)

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

Additional Attributes:

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

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

PredictLeads Dataset: https://docs.predictleads.com/v3/guide/job_openings_dataset

Hourly rate steps for all stepped salary plans. See the City Employment Classification Definitions dataset for job descriptions and minimum qualifications for each position.

📊 Glassdoor Data for U.S. Salary Intelligence, Executive Compensation & HR Strategy Glassdoor Data is one of the most actionable and trusted sources of alternative data for understanding salary benchmarks, executive compensation, and company-level payroll dynamics. At Canaria Inc., we've enhanced and structured raw Glassdoor Data into a matchable, high-quality dataset that supports advanced compensation modeling, HR analytics, financial strategy, and company analysis.

Our enriched Glassdoor Data provides detailed salary estimates, executive pay signals, and employee ratings across thousands of U.S. companies. Each company record is normalized and includes verified identifiers, industry tags, and public metadata. To further increase precision and usability, we match each company with Google Maps data — enabling geographic insights such as office location, branch metadata, and review context.

This salary and payroll data product is designed for compensation strategists, HR teams, market analysts, and financial professionals looking to model workforce costs, track pay trends, and benchmark companies across industries and regions.

🧠 Use Cases: What Problems This Glassdoor Data Solves Whether you're adjusting salary bands, modeling pay trends, benchmarking executive compensation, or integrating compensation insights into a portfolio strategy, this dataset helps teams replace guesswork with evidence-backed decision-making.

💰 Compensation Benchmarking & Strategy • Benchmark base salary, executive compensation, and payroll trends by industry • Understand compensation differences by company size, structure, and market segment • Compare companies based on leadership pay, employee ratings, and public sentiment • Improve transparency and equity across internal salary bands with external data • Support DEI and gender pay equity initiatives with data-backed validations

📈 Financial Intelligence & Valuation Modeling • Integrate salary and payroll estimates into DCF or profitability models • Use leadership compensation data to evaluate fiscal responsibility or growth maturity • Assess pay-to-revenue ratios for private companies or startup valuation proxies • Track cost structures across competitors and industries using public salary trends • Correlate high executive pay or high salary growth with hiring trends and expansion risk

📊 HR Analytics & Payroll Planning • Use Glassdoor Data to calibrate compensation plans, bonuses, and incentive structures • Align headcount forecasting with real-world salary benchmarks • Benchmark benefits, perks, and compensation packages across employers • Monitor hiring sentiment and satisfaction through employee reviews and scores • Analyze which companies are retaining employees via positive review trends

🔍 Company Analysis & Leadership Trends • Monitor leadership hiring and compensation levels across mid-size and enterprise firms • Use CEO and executive pay benchmarks to compare strategic leadership investment • Connect compensation signals with business growth stage and industry maturity • Validate company credibility and financial practices using Glassdoor transparency signals • Detect early warning signs in company health through review count declines or rating shifts

🌐 Matchable Glassdoor Data with Google Maps & Company Profiles Our Glassdoor Data product is enhanced with matchability to company profiles and location intelligence — turning salary insights into full company intelligence.

📍 Match with Google Maps • Each record includes location-aware metadata such as ZIP code, coordinates, and physical address • Connect salary insights with Google Maps business categories and branch distribution • Identify executive pay variations across headquarters vs. regional offices • Power ABM (account-based marketing) and location-specific compensation modeling

🔗 Match with Company Profiles • Linked with LinkedIn company URLs, size ranges, and industry classifications • Fully structured data that joins seamlessly with job postings, revenue, or valuation datasets • Company keys allow you to analyze salary vs. hiring, sentiment vs. headcount, and more • Extend Glassdoor salary data into broader firmographic or market research projects

🔗 Data Quality, Delivery & Enrichment Canaria’s Glassdoor Data is built for seamless delivery and fast integration into enterprise systems.

• Clean, deduplicated, and normalized data • Filterable by company size, industry, review count, or compensation level • Scalable to match with job postings, HRIS, CRM, or BI tools • Updated monthly to track compensation shifts and company ratings in near real-time

🎯 Who Uses Canaria’s Glassdoor Data? • HR & People Analytics Teams modeling compensation benchmarks • Finance Teams & Controllers tracking labor costs and comp-to-revenue ratios • Recruiters & Talent Teams refining offers based on market pay expectations • Private Equity & VCs modeling operating costs and salary risks • Compensation Consultants building sala...

H-1B visa sponsorship trends for Senior Data Scientist, covering top employers, salary insights, approval rates, and geographic distribution. Explore how job title impacts the U.S. job market under the H-1B program.

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.