SAMPLE Employee Data|Reviews, Skills & Salary from 800M+ US Job Records|Enriched Employee Data ...

Most employee datasets are incomplete. Compensation surveys miss low-volume roles. Review platforms give you averages, not raw records. Skills databases are flat keyword lists with no context. Our Employee Data combines what employees say, what they earn, and what skills they bring to market.

This Employee Data is built from structured Glassdoor and Indeed reviews matched with AI-enriched job postings data covering 800M+ deduplicated US records. Every record is matched at the company level, so you can connect employee sentiment to hiring activity, skill demand, and compensation in one dataset.

What This Employee Data Covers

This Employee Data product combines review data with enriched job postings data matched at the company level.

Employee Data Reviews (Glassdoor and Indeed):

• Overall company rating (1-5 stars)
• Sub-ratings: work-life balance, pay and benefits, job security, management, culture
• Review title, pros text, and cons text (full content)
• Reviewer job title, location, and employment status (current or former employee)
Employee Data Skills and Employment Records:

• Normalized job title (from 50,000+ standardized categories)
• Hard skills (37,000+ unique) with weighted relevance scores
• Certifications (3,000+) and soft skills (400+)
• Seniority level (Entry, Mid, Senior, Lead, Executive)
• Work type (Remote, Hybrid, Onsite) and employment type
• Salary (parsed from posting and AI-predicted where missing)
This Employee Data covers the United States from 2022 to present, updated weekly.

What You Can Do With This Employee Data

Compensation Benchmarking and Pay Equity

HR and compensation teams use Employee Data to benchmark salaries, validate internal pay structures, and identify equity gaps across roles, seniority levels, and locations. Employee Data combines what companies post as salary with AI predictions for the 60-70% that don't show pay, giving you full compensation coverage.

• Benchmark base pay by role, seniority, and location using Employee Data salary and prediction fields
• Validate internal compensation structures against external Employee Data records
• Analyze competitor compensation levels through Employee Data review sub-ratings for pay and benefits
• Build compensation bands grounded in Employee Data actual posting and predicted salary ranges
Employer Brand and Talent Attraction

Recruiting and employer brand teams use Employee Data review signals to monitor culture perception, track satisfaction trends, and benchmark employer reputation against competitors. Employee Data gives you the raw review data, not just an aggregate score, so you can understand the drivers of sentiment.

• Monitor employer reputation trends using Employee Data review ratings and sub-scores over time
• Compare culture scores, management ratings, and work-life balance signals across competitors with Employee Data
• Identify emerging retention risks through shifts in Employee Data review sentiment by company and role
• Benchmark your Employee Data employer brand signals against industry peers
HR Analytics and People Intelligence

People analytics teams use Employee Data to combine internal HR data with external market signals, connecting what employees say publicly with what skills and pay the market is offering. Employee Data at the company level gives analysts a 360-degree view of the talent environment.

• Correlate Employee Data review sentiment with hiring velocity as an indicator of company health
• Track skill demand trends by role and industry using Employee Data skills and certification fields
• Benchmark internal HR metrics against external Employee Data market signals
• Identify high-retention employers through Employee Data culture and management sub-ratings
Investment and Due Diligence

Investors and deal teams use Employee Data as a leading indicator of organizational health. Shifts in Employee Data review scores often reflect culture changes or leadership transitions before they appear in financial filings.

• Monitor portfolio company employee sentiment using Employee Data review scores over time
• Use Employee Data culture and management ratings as early signals of leadership or operational risk
• Assess private company workforce health through Employee Data review patterns and hiring activity
• Track compensation competitiveness of portfolio companies using Employee Data salary benchmarks
How We Build This Employee Data

The salary estimation model fills the pay gap in Employee Data records. About 60-70% of job postings do not include explicit salary. Trained on 50M+ observations with a MAPE under 15%, it predicts ranges using company history, geographic adjustments, role seniority, and industry norms, giving Employee Data full salary coverage across all records.

The title taxonomy model normalizes every Employee Data job title into 50,000+ standardized categories from 20M+ raw titles, enabling consistent comparison of skills and salaries across companies, industries, and geographies.

The skill taxonomy model extracts 37,000+ hard skills, 3,000+ certifications, and 400+ soft skills from Employee Data job postings using contextual AI filtering with weighted relevance scores to separate core skills from peripheral mentions.

Job category models classify each Employee Data record by seniority (Entry, Mid, Senior, Lead, Executive) and work modality (Remote, Hybrid, Onsite) using LLM-based analysis of the full job description.

Our annotation team validates every Employee Data model output before delivery.

What Makes This Employee Data Different

• Three-dimensional: Employee Data combines reviews, skills, and salary in one company-matched dataset
• Raw records: Employee Data delivers individual review records, not aggregated scores, for granular analysis
• Weighted skills: Employee Data distinguishes core skills from peripheral mentions using relevance scores
• Full salary coverage: Employee Data AI predictions fill the 60-70% of records without explicit pay, with MAPE under 15%
• Company-matched: every Employee Data review and skill record links to a verified company profile
• Weekly updates: Employee Data refreshes weekly so sentiment and skill demand signals reflect current market conditions
Who Uses This Employee Data

• HR and Compensation Teams use Employee Data to benchmark salaries, validate pay equity, and track external compensation trends against internal structures
• People Analytics Teams use Employee Data to correlate internal HR metrics with external market signals for a fuller view of the talent environment
• Employer Brand and Recruiting Teams use Employee Data review signals to monitor reputation, track culture trends, and benchmark against competitor employers
• Investors, PE Firms, and VC Funds use Employee Data sentiment and hiring signals as alternative data for due diligence and portfolio monitoring
• Learning and Development Teams use Employee Data skills records to design training programs aligned with real employer demand and market skill trends
• Compensation Consultants use Employee Data to advise clients on market rates, pay equity, and competitive compensation positioning
• HR Tech Companies and B2B Platforms use Employee Data to power compensation benchmarking tools, employer review products, and talent intelligence applications
Delivery and Format

Employee Data is delivered in CSV, JSON, or Parquet via AWS S3 or Google Cloud Storage. Custom filters by company, geography, date range, industry, role, seniority, review rating, and employment status. Compatible with Snowflake, Databricks, Power BI, Tableau, Salesforce, and most BI platforms.