Salary Research Guide

Reviewed by Owen Barrister (OB), Editor-in-Chief — Compensation Strategy & Career Negotiation Practice. Updated May 2026.

A salary negotiation without data is a guess. A salary negotiation with data is an argument. The difference — in confidence, in outcome, and in the employer’s perception of the candidate — is significant. This guide explains how to build a defensible, data-backed salary case using free and paid research tools.

Primary Salary Research Tools

Glassdoor

Glassdoor is the most widely recognized salary research platform, with self-reported salary data from employees across industries, company sizes, and geographic locations. Glassdoor’s strengths: broad coverage across almost every industry and role type; geographic filtering down to metropolitan area; experience-level filtering; and company-specific data that shows what a specific employer pays for a specific role. Glassdoor’s limitations: data is self-reported and may lag current market conditions by 12–18 months; the quality of data varies significantly by industry and company size (technology companies are well-covered; smaller companies in less-represented industries are poorly covered); and there is potential for inflation bias (people who report their salaries may over-represent the higher end).

Best use of Glassdoor: establish a baseline range for your role and location, particularly for non-technology industries where other tools have weaker coverage. Cross-check with at least two other sources before relying on Glassdoor numbers for your negotiation.

Levels.fyi

Levels.fyi is the most accurate and detailed salary database for technology roles at major technology companies. It tracks total compensation (base salary, bonus, and equity) by company and job level (L3, L4, L5, etc. at Google/Meta; E3, E4, etc. at Meta; Senior SWE, Staff SWE, etc.) with data updated in near-real-time as users share verified offer data. Levels.fyi’s strengths: highly accurate total compensation data for technology roles; visibility into the equity component (RSUs) that often exceeds base salary at major tech companies; company-specific data that allows direct comparison across competing offers. Limitations: primarily covers large technology companies; coverage outside technology is limited.

Best use of Levels.fyi: anyone in a technology role (software engineering, product management, data science, machine learning, design) negotiating with any company for which Levels.fyi has data should use it as the primary salary research tool. The total compensation transparency it provides is far more valuable than base-salary-only data for technology negotiations.

LinkedIn Salary

LinkedIn Salary provides salary data aggregated from LinkedIn user profiles, with filtering by job title, location, years of experience, industry, company size, and education level. Strengths: excellent coverage for professional and business roles that are not well-represented on Levels.fyi; location filtering is granular and accurate; the ability to filter by specific years of experience produces more targeted data than tools with only broad experience bands. Limitations: some title inflation in self-reported data; coverage thinner for highly specialized technical roles compared to Levels.fyi.

Best use of LinkedIn Salary: research for non-technology professional roles — finance, marketing, operations, HR, legal, and general management. Often the most useful tool for roles at smaller or less well-covered companies.

BLS Occupational Employment Statistics (OES)

The Bureau of Labor Statistics Occupational Employment and Wage Statistics program provides annual salary data for hundreds of occupations broken down by metropolitan statistical area, state, and industry. BLS data is the most authoritative public-sector salary source available — it is collected through a survey of employers rather than self-reporting, which reduces the bias that affects user-reported platforms. BLS strengths: authoritative, government-collected data; geographic breakdown to the MSA level; industry breakdown that shows salary variation by sector for the same occupation. Limitations: data lags by approximately 12–18 months (the most recent OES data reflects survey results from the prior year); occupation categories are broader than the specific job titles used in private-sector hiring; and data does not distinguish between levels (junior vs. senior) within an occupation category.

Best use of BLS OES: cross-check and validate data from commercial platforms, particularly for roles where self-reported data quality is uncertain. The BLS percentile data (10th, 25th, 50th, 75th, 90th percentile wages) is especially useful for identifying where in a market distribution a specific ask falls.

Payscale

Payscale aggregates salary data with a focus on skills-based analysis — how specific skills and certifications affect compensation within a role. Payscale’s strength is its skills-based breakdown: for roles where specific technical certifications or skills carry significant wage premiums (cybersecurity certifications, specific programming languages, clinical certifications in healthcare), Payscale often provides the most useful data on those premiums. Limitation: data quality is inconsistent across industries and roles.

Your Network: The Highest-Quality Source

Published salary databases aggregate and average data that may be months or years old. Your professional network provides current, specific, verified information about what people in your exact situation are actually being paid right now. A colleague who was just hired at a comparable company, a peer who recently negotiated a similar role, or a mentor in the same industry who can share benchmarks — these conversations produce salary intelligence that no database can match.

Sharing salary information with colleagues and peers is legally protected under the National Labor Relations Act (NLRA), which prohibits employers from retaliating against employees who discuss wages with each other. Despite some employers’ attempts to create norms against salary discussion, you have a legal right to share and discuss your compensation with peers. Online communities — the Blind app for technology professionals, industry-specific forums and Slack communities, professional associations — have developed cultures of salary transparency that provide practical benchmarks for specific roles and companies.

How to Read a Salary Band

Most companies structure compensation within salary bands for each job level — a minimum, a midpoint, and a maximum. Understanding band structure helps you negotiate to the right point within the range rather than above it (which is usually not possible) or below it (which unnecessarily reduces your compensation).

Typical salary band structure: the band spans approximately 50–60% from minimum to maximum (a band might run from $80,000 to $125,000 for a given level). The midpoint represents the "fully proficient" rate — what the company pays an employee who is fully competent in the role. New hires typically start at 40th–60th percentile of the band, reflecting the expectation that they will continue to develop. Negotiating to the 60th–70th percentile of the band for a new hire who is highly qualified and immediately productive is achievable and appropriate. Asking for above the band maximum is rarely possible within the same level, but may be addressed by negotiating for a higher starting level.

When posted salary ranges are available (required in states like Colorado, New York City, and California for positions that can be performed remotely in those states), the posted range is the band. Your research should focus on where in that band you should be placed based on your qualifications — not on whether the posted range is market rate.

Building Your Negotiation Data Package

Before your negotiation conversation, compile your salary research into a concise case you can reference and, if useful, share. The data package should include: the salary range for your role and location from 3–5 sources; your placement in the distribution based on your years of experience and specific skills; the specific companies and roles you are referencing (particularly if any are direct competitors of the employer); and any premium factors relevant to your specific situation (in-demand skills, competing offer, specialized credentials). You do not need to produce a spreadsheet in the conversation — but having the data organized means you can cite specific sources when the employer questions your number.

Return to the calculator, see how to negotiate salary, or read the FAQ.