Monthly advertised-salary percentiles and top requested skills for 800 occupations in 390 US and Canadian metro areas, built from posted pay ranges and calibrated to official wage data.
Data product · Listing updated 11 Nov 2026
Canada, United States
Published on the fifth business day of each month
About 800 occupations across 390 metros; 1.4 million benchmark cells a month
Small and medium businesses, Mid-market, Enterprises, Public sector, Academia and research
Salary ranges in deduplicated job postings, calibrated against public occupational wage statistics
Monthly aggregation with minimum cell sizes and calibration to government wage surveys
Current, local pay benchmarks that move faster than annual government surveys.
For each occupation, metro area and month: the number of postings, 25th, 50th and 75th percentile advertised annual salary, share of remote or hybrid roles, the 15 most requested skills with their frequency, and year-on-year change in median pay. National and state or provincial roll-ups are included. Occupations follow SOC 2018 in the US and NOC 2021 in Canada, with a crosswalk for cross-border comparisons.
Benchmarks are computed from salary ranges in our deduplicated postings feed. Pay-transparency rules in several US states and Canadian provinces have raised the share of postings with salary, but coverage still varies by occupation and region, so each cell shows its posting count and cells with fewer than 30 postings are suppressed. Hourly and monthly pay is annualized using standard hours. Medians are calibrated against public occupational wage statistics to correct for occupations where advertised pay runs above or below actual pay. Files are published on the fifth business day of each month.
13 fields as documented by Maplecourt Workforce Data.
| Field | Type | Description | Example |
|---|---|---|---|
| occupation_code | string | SOC 2018 or NOC 2021 code | — |
| occupation_title | string | Occupation name | — |
| metro_code | string | CBSA (US) or CMA (Canada) code | — |
| metro_name | string | Metro area name | — |
| country_code | string | US or CA | — |
| month | date | First day of the reference month | — |
| posting_count | integer | Postings with salary in the cell | — |
| salary_p25 | number | 25th percentile advertised annual salary | — |
| salary_p50 | number | Median advertised annual salary | — |
| salary_p75 | number | 75th percentile advertised annual salary | — |
| top_skills | array | Most requested skills with frequency | — |
| remote_share | number | Share of remote or hybrid postings | — |
| yoy_change_pct | number | Change in median pay versus the same month last year | — |
Free sample available on request
The provider sends a sample file so you can test fit and quality before you buy.
Prices in USD, as published by Maplecourt Workforce Data
one country, all occupations
US and Canada with history and dashboard
Converted amounts use DataStoa display rates and are approximate. Maplecourt Workforce Data quotes the final price, taxes and terms.
Request detailed pricing2 countries.
Time coverage: 2019 to present.
Provider-reported
Latency: Published on the fifth business day of each month
DataStoa has not audited these figures. Ask the provider how they are measured, or test them on a sample.
The licence is agreed directly between you and Maplecourt Workforce Data. This summary is provider-reported; the linked terms prevail.
Not listed: use in commercial products, AI / ML model training, AI retrieval and grounding, redistribution allowed, marketing and outreach. Ask Maplecourt Workforce Data if you need any of these.
Job postings, wages and hiring signals for North American employers
Maplecourt collects and deduplicates job postings from employer career sites and job boards across the US and Canada, then turns them into wage benchmarks and company-level hiring signals. HR, strategy and investment teams use it to see where demand for skills is moving.
Also available on: Snowflake Marketplace, AWS Data Exchange
What buyers say about Maplecourt Workforce Data. Reviews cover the provider, not only this listing.
from 2 reviews
The ticker-mapped hiring velocity series is point in time and the mapping handles subsidiaries sensibly. It improved one of our sector models modestly, which is about what we hoped. Delivery into our bucket has been on time every week.
Kevin L., Data scientist, Quant equity fund
The same job reposted on several boards shows up once with all its sources attached, so our demand counts are no longer inflated. Salary fields are sparse outside states with pay transparency laws, which they document clearly.
Rachel N., Talent intelligence analyst, Staffing group