Tech Layoffs 2026: How to Scrape Salary Data and Job Market Intelligence with ScrapeMaster

TL;DR

Tech cut 80,000+ jobs in Q1 2026—nearly half attributed to AI—and the job market intelligence available online has never been richer or more important. ScrapeMaster is a free, no-code Chrome extension that lets you scrape salary data from Levels.fyi and LinkedIn Salary, extract layoff tracker data from public sources, and pull job listings from multiple boards into a single spreadsheet—all without writing code, creating accounts, or hitting usage limits. This guide walks through specific workflows for job seekers and hiring managers navigating the 2026 market.

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The 2026 Tech Job Market: By the Numbers

The data on Q1 2026 tech layoffs is clear and striking:

• 78,557 to 95,278 workers laid off from tech companies in Q1 2026
• 47.9% of cuts directly attributed to AI automation
• Oracle: 20,000-30,000 employees cut in a single round
• Block: 4,000 jobs (40% of global workforce) eliminated
• Laid-off workers face one month longer job searches and 3%+ real earnings loss on reemployment compared to workers displaced from stable sectors

Both job seekers and hiring managers need better data:

Job seekers need: realistic salary benchmarks by role and market, which companies are still hiring vs. freezing, what roles are growing vs. contracting, and what the true compensation spread looks like for their target positions.

Hiring managers need: competitive compensation intelligence, candidate availability trends, and market rate data to build or adjust compensation bands as AI reshapes role definitions.

All of this data is publicly available online—on salary transparency sites, layoff tracking databases, job boards, and industry analysis pages. The question is how to extract it efficiently.

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Salary Data Sources Worth Scraping

Levels.fyi

Levels.fyi has become the most trusted source of software engineering compensation data. It shows total compensation (base salary + bonus + equity) broken down by company, level/title, location, and years of experience. The data is crowd-sourced and extensive.

What to scrape: Compensation data for your target role and company set. The table views on Levels.fyi are well-structured for extraction—ScrapeMaster auto-detects the data grid.

Workflow:

1. Navigate to the Levels.fyi page for your target role/company
2. Open ScrapeMaster and let it auto-detect the data structure
3. Export to CSV or directly to clipboard
4. Repeat for multiple companies and aggregate the data

LinkedIn Salary

LinkedIn Salary shows compensation ranges for specific job titles at specific companies, filtered by location and experience level. The data has become more reliable as more members add salary information.

What to scrape: Job title salary ranges for roles you're targeting or hiring for. The page shows a distribution chart and a table of reported salaries—the table is scrapeable.

Glassdoor

Glassdoor combines salary data with company reviews and interview process information. For hiring managers, scraping the interview experience and culture data alongside compensation can provide a richer competitive intelligence picture.

BLS Occupational Employment Statistics

The Bureau of Labor Statistics publishes detailed occupational employment and wage statistics. These are more lag-prone than crowd-sourced data but provide official market rate benchmarks useful for compliance (particularly for pay equity analyses).

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Layoff Tracker Sites Worth Monitoring

Multiple public layoff tracking databases have emerged since 2022. The most useful for market intelligence:

Layoffs.fyi / TrueUp Layoffs Tracker

These aggregated databases track announced layoffs by company, date, number of employees affected, and stated reason. The data is valuable for:

• Understanding the pace of cuts in specific industry segments
• Identifying when talent may be entering the market from specific companies
• Tracking whether your own industry segment is contracting

ScrapeMaster can extract the table data from these public tracker pages, allowing you to build a local database of layoff events filtered to your relevant industry segment.

LinkedIn's Layoff Reporting

LinkedIn's own platform surfaces layoff news through company updates and news feeds. While not a dedicated database, the structured format of LinkedIn news pages allows for data extraction.

Crunchbase

Crunchbase tracks layoff announcements alongside funding data, allowing you to cross-reference whether companies cutting staff are also raising money—a signal about whether cuts are opportunistic (AI-driven efficiency) or distressed (financial stress).

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How ScrapeMaster Works for Job Market Data

ScrapeMaster is an AI-powered web scraping Chrome extension designed for non-technical users. Here's what makes it effective for job market research:

Auto-Detection of Data Structures

Navigate to a salary table or layoff tracker page and ScrapeMaster automatically identifies the data structure—columns, rows, pagination. You don't need to write CSS selectors or XPath queries. Click "Detect" and it maps the table.

Pagination Handling

Salary databases and layoff trackers typically paginate across many pages. ScrapeMaster handles multi-page scraping automatically—select "Follow pagination" and it will extract data across all pages, not just the first.

Multiple Export Formats

Extracted data can be exported as:

• CSV: For further analysis in Excel or Google Sheets
• XLSX: Native Excel format
• JSON: For programmatic processing
• Clipboard: For immediate pasting into any application

No Limits, No Account

ScrapeMaster is free with no usage limits and requires no account or API key. You can run it as many times as you need—for daily salary monitoring, weekly layoff tracker updates, or ad-hoc company-specific research.

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Job Seekers: Building Your Market Intelligence Toolkit

Salary Negotiation Data Package

For a salary negotiation, you want:

1. Role-specific data: What does your exact title and level pay at comparable companies?
2. Location adjustment: How does compensation vary by metro area?
3. Total compensation breakdown: What are the ratios of base/bonus/equity at your target companies?
4. Trend data: Is compensation for this role increasing or decreasing?

Scraping workflow:

1. Scrape Levels.fyi for your role across 10-15 comparable companies → save as role_comp_YYYY-MM-DD.csv
2. Scrape LinkedIn Salary for the same role → save as linkedin_salary_YYYY-MM-DD.csv
3. Merge in a spreadsheet and calculate percentiles
4. Use the data explicitly in negotiations: "Based on public market data from [date], the median total comp for this role at companies of this stage and size is..."

Company Health Screening

Before applying to a company, check:

1. Is the company on any layoff tracker with recent cuts?
2. What is their funding status on Crunchbase?
3. Are there patterns in recent Glassdoor reviews (sudden exodus, culture concerns)?

ScrapeMaster can pull each of these data points, and you can build a simple screening scorecard.

Tracking Your Target Company List

If you're applying to multiple companies, maintaining a spreadsheet of salary data, hiring status, and company health for each target can be tedious. Scraping directly into CSV and then pasting into a master spreadsheet is faster than manual entry.

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Hiring Managers: Competitive Intelligence for Compensation

Compensation Band Benchmarking

For HR and compensation teams, building up-to-date compensation bands requires regular market data collection. Scraping Levels.fyi quarterly (or monthly during high-volatility periods) provides the data needed to maintain competitive offer packages.

Workflow:

1. Create a master list of your key roles and the comparable company set
2. Schedule a monthly scraping session using ScrapeMaster
3. Append new data to your historical dataset with date stamps
4. Identify drift from market rates before you start losing candidates

Talent Availability Monitoring

When a major tech company announces layoffs, there's typically a surge in talent availability in the following 3-6 months. Monitoring layoff tracker sites helps hiring managers anticipate when specific talent pools will be active.

For example: when Oracle announced 20,000-30,000 cuts in 2026, hiring managers at Oracle-adjacent companies (enterprise software, cloud infrastructure, databases) had advance warning that strong candidates would be on the market.

Understanding the AI-Attribution Pattern

With 47.9% of Q1 2026 cuts attributed to AI automation, there's a signal in the data about which skill sets are most at risk. Tracking the stated reasons for layoffs across multiple companies gives a pattern:

• Roles being cut: data entry, certain QA functions, basic code review, customer support tier 1
• Roles being created: prompt engineering, AI QA, AI safety, AI product management

Scraping the reason/attribution data from layoff trackers into a spreadsheet reveals these patterns at scale.

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Comparing ScrapeMaster to Other Job Market Research Tools

Tool	No-Code	Free	Data Export	Auto-Pagination	Account Required
ScrapeMaster	Yes	Yes	CSV/XLSX/JSON	Yes	No
Instant Data Scraper	Yes	Yes	CSV	Limited	No
Web Scraper (webscraper.io)	Partial	Limited	CSV	Yes	Yes
Octoparse	Partial	Free tier limited	CSV/Excel	Yes	Yes
ParseHub	No	Free tier limited	JSON/CSV	Yes	Yes
Import.io	No	Paid	CSV	Yes	Yes
Manual copy/paste	N/A	Free	Clipboard	No	N/A

For job seekers and hiring managers who want to extract salary and layoff data without paying for an enterprise scraping tool or learning to code, ScrapeMaster is the strongest option.

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Staying Within Legal and Ethical Boundaries

Scraping publicly available salary data and layoff tracker data from sites like Levels.fyi, Glassdoor, and public news aggregators is generally lawful. These sites publish the data for public consumption, and accessing public web pages with automated tools is typically not prohibited.

However:

• Review each site's Terms of Service before scraping at scale
• Don't circumvent authentication systems or attempt to access data not available without login
• Scrape at a respectful rate—don't hammer servers with thousands of requests per minute
• Use the data for your own analysis, not for commercial redistribution

For job seekers doing personal market research and hiring managers doing compensation benchmarking, these guidelines are easy to follow.

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Frequently Asked Questions

Is it legal to scrape salary data from Levels.fyi and Glassdoor?

Scraping publicly available salary data for personal research and analysis is generally lawful under the principle that public web pages can be accessed by anyone. The 2024 court rulings in hiQ Labs v. LinkedIn and related cases affirmed that scraping public data does not violate the Computer Fraud and Abuse Act (CFAA). Review each site's Terms of Service for their specific policies on automated access.

Can ScrapeMaster handle the JavaScript-heavy interfaces on Levels.fyi?

Yes. ScrapeMaster operates as a Chrome extension, using Chrome's full rendering engine to process JavaScript-rendered pages. Unlike server-side scrapers that might see only the raw HTML, ScrapeMaster sees the same rendered page you do.

How often should I update my salary benchmarks?

During volatile periods (like the current AI-driven layoff wave), monthly updates are valuable. For stable markets, quarterly updates are typically sufficient for compensation benchmarking purposes.

Can I scrape multiple companies' salary data in one session?

Yes. Open each salary data page in a separate tab and run ScrapeMaster on each. Export each as CSV, then merge the files in Excel or Google Sheets.

Does ScrapeMaster store my scraped data anywhere?

No. ScrapeMaster processes data locally in your browser and exports directly to your device. No data is sent to any server.

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Bottom Line

80,000 tech layoffs in Q1 2026, with AI automation driving nearly half the cuts, has created intense demand for accurate job market intelligence. The data exists—on salary transparency sites, layoff trackers, and job boards—but gathering it manually is time-prohibitive.

ScrapeMaster makes this data collection practical: no-code, no account, no limits, with auto-pagination and multiple export formats. Whether you're a job seeker building your negotiation case or a hiring manager setting competitive compensation bands, the data you need is one scrape away.

Navigate the 2026 market with data, not guesswork.