How to scrape salary data (and why compensation researchers should build this into their workflow)
- Minexa.ai
- 21 hours ago
- 5 min read
Salary data is publicly available on dozens of platforms. The problem is not access. The problem is that reading it page by page, role by role, does not scale. Compensation researchers who need structured, comparable data across hundreds of roles end up spending most of their time copying and formatting rather than actually analysing.
This guide covers how to extract salary data from role detail pages using the Minexa.ai Chrome extension, what fields you can expect to capture, and how that data fits into a real compensation research workflow.
What data is available on a salary detail page
A typical role detail page on a salary platform contains more structured information than most people realise. Beyond the headline figure, there is usually a full breakdown that includes:
Base salary range (minimum, median, maximum)
Total compensation (base plus bonus, equity, or other components)
Role title and seniority level
Industry or sector classification
Geographic location or region
Years of experience brackets
Company size or type
Bonus percentage or range
Number of data points or survey respondents
Last updated date
Not every platform surfaces all of these fields, but most detail pages contain at least six to eight of them. Minexa detects and ranks all available fields automatically, so you do not need to know in advance exactly what is on the page before you start.
How the extraction works: step by step
Salary detail pages are a good example of where the two-layer scraping approach matters. The list page gives you role names and top-level figures. The detail page gives you the full breakdown. Here is how to get both in a single run:
Install the Minexa.ai Chrome extension from the Chrome Web Store.
Browse to the salary listing page for the category or role type you want to research (for example: https://salarydata.com/role/123).
Minexa detects the page structure automatically. It identifies the list of roles, all data points within each result, and the pagination method the site uses, whether that is a next page button, infinite scroll, or a load more button.
Confirm what Minexa found. This is a short yes/no confirmation step. You are not selecting fields manually. Minexa has already identified them.
Enable detail page extraction. At the confirmation step, you have the option to instruct Minexa to follow each role link and extract the full detail page as well. This is where the complete salary breakdown lives. Enable this to get every field from every role detail page in one run.
Run the job. Minexa processes each detail page and returns a single structured dataset.
Export to Excel, Google Sheets, or JSON. Each role becomes one row. Each data point becomes one column.
The first time you run this on a salary platform, Minexa trains on the page structure. That training takes a few seconds to a few minutes. After that, any page with the same structure is processed almost instantly. You do not repeat the setup for every role or every page.
What the output looks like
After a single run across a category of roles, you end up with a clean spreadsheet where:
Each row is one role
Each column is one data point from the detail page
Fields like salary range, location, experience bracket, and last updated date are all in separate, queryable columns
The data reflects exactly what was on the page at the time of extraction, with no reformatting or interpretation applied
If a field is not present on a particular page, that cell is empty. Minexa does not fill in missing values or make assumptions. This matters for compensation research because a fabricated figure in a salary dataset is worse than a blank cell.
Why this matters for compensation researchers specifically
Compensation research involves comparing structured data across many roles, levels, and geographies. The challenge is not finding salary information. It is getting that information into a format where you can actually work with it. Here is where structured extraction changes the workflow:
Building pay bands: Instead of manually reading and recording figures for each role, you extract a full category of roles in one run and use the resulting dataset to define or validate pay bands across levels.
Benchmarking against market data: With a structured dataset covering dozens or hundreds of roles, you can compare internal compensation against external benchmarks without relying on expensive survey subscriptions for every data point.
Geographic pay analysis: Many salary platforms include location fields in the detail page. Extracting these alongside compensation figures lets you build regional comparisons without manual cross-referencing.
Equity and fairness analysis: Structured data across roles and levels gives you the foundation to identify gaps, inconsistencies, or outliers in a way that is difficult to do from manually gathered figures.
Tracking changes over time: Salary data shifts with market conditions. Scheduled runs let you capture the state of a salary page at regular intervals, building a longitudinal view of how compensation for a given role has moved. You set this up once and it runs automatically on whatever cadence makes sense for your work.
A note on data accuracy
Minexa extracts data based on the structure of the page, not by reading and interpreting the content. This means:
Each field is tied to a specific position in the page structure
The same field returns the same value every time, across every page of the same type
There is no risk of a minimum salary figure being assigned to the maximum column, or a bonus figure being confused with a base salary
This is particularly relevant for salary data, where multiple numeric fields appear on the same page and the distinction between them matters. Tools that interpret page content rather than reading structure can occasionally assign the wrong value to the wrong field without flagging the error. Minexa avoids this entirely because the extraction is tied to position, not interpretation.
Scheduling for ongoing research
Compensation benchmarking is not a one-time task. Market rates shift, new roles emerge, and the data you collected six months ago may no longer reflect current conditions. Minexa lets you schedule any scraping job to run automatically on a recurring basis:
Set a daily, weekly, or monthly schedule after the initial run
Each run captures the current state of the page at that moment
Results accumulate over time, giving you a historical record of how salary figures for specific roles have changed
No manual triggering required after the initial setup
For compensation researchers who need to present current, defensible market data, this removes the need to re-run the process manually every time a benchmark review comes around.
Getting started
If you have never used Minexa before, the setup is straightforward:
Install the Minexa.ai Chrome extension
Browse to any salary platform role listing page
Let Minexa detect the structure automatically
Enable detail page extraction at the confirmation step
Run the job and export your first dataset
Most users have a structured dataset exported within a few minutes of their first run. The scraper you create in that session can be reused on any structurally similar page without repeating the setup.
For more on how detail page extraction works across different data types, see: Hotel listing data for travel startups: what you can collect and why it changes how you build.
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