How to get H-1B & visa sponsorship data

There are two practical ways to work with US H-1B and labor-certification data: download and parse the Department of Labor's quarterly OFLC disclosure files yourself, or call one normalized API. This page compares them honestly so you can pick the right one.

Get API key on RapidAPI SponsorLens docs

Short answer

The DOL OFLC files are public, so parsing them yourself is viable for a one-off analysis of a single program and fiscal year. But for anything ongoing — multiple programs, cross-year history, annualized wage comparisons, or "does this company sponsor?" lookups — the quarterly, schema-shifting Excel dumps become a recurring ETL chore, and a normalized API like SponsorLens is the cheaper path. The dividing line is how many programs and years you need, and whether you need wages comparable across roles.

Why the raw DOL files are painful

The data is public, but it ships as bulk spreadsheets built for archival disclosure, not for querying.

One file per program per year

H-1B, PERM, H-2A, and H-2B are each separate multi-megabyte Excel files, released per fiscal year. A national, multi-program view means stitching many files together.

Schema shifts between releases

Column names and layouts change between quarterly disclosures. A parser tuned to one release can break on the next without warning.

Wages aren't comparable as-published

Offered and prevailing wages are recorded as hourly, weekly, monthly, or yearly with separate unit-of-pay columns — you must annualize them before any benchmarking makes sense.

Hundreds of columns, no API

Each row has hundreds of fields and there's no query layer — you load, clean, and index everything yourself before you can answer a single question.

Side by side

Parsing the DOL OFLC disclosure files yourself vs. calling the SponsorLens API.

	Parse DOL files yourself	SponsorLens API
Programs	Separate file per program; combine yourself.	H-1B, H-1B1, E-3, PERM, H-2A, H-2B on one endpoint.
Format	Multi-MB Excel, hundreds of columns.	One JSON schema across all programs.
Schema stability	Shifts between quarterly releases.	Stable, normalized fields.
Wages	Hourly/weekly/monthly/yearly — annualize yourself.	Offered + prevailing wages annualized for you.
Querying	Load, clean, and index before any lookup.	Filter by `employer`, `program`, occupation, wage in the request.
Provenance	You track which file each row came from.	Every record carries `source_url` to the OFLC file.
Upfront cost	Build an ETL pipeline before any answer.	Minutes — get a key and query.
Best when	One-off analysis of a single program/year.	Ongoing, multi-program, cross-year, wage-comparable use.

What the API call looks like

"Does NVIDIA sponsor H-1B?" — one request instead of an ETL pipeline.

NVIDIA H-1B certifications

curl -X GET "https://sponsorlens-h-1b-labor-cert-api.p.rapidapi.com/v1/certifications?employer=nvidia&program=h-1b&limit=1" \
  -H "X-RapidAPI-Key: YOUR_KEY_HERE" \
  -H "X-RapidAPI-Host: sponsorlens-h-1b-labor-cert-api.p.rapidapi.com"

See the full request examples (cURL, JavaScript, Python) and the OpenAPI spec ↗ on the SponsorLens page.

Frequently asked questions

Where does H-1B sponsorship data come from?

It comes from the US Department of Labor's Office of Foreign Labor Certification (OFLC), which publishes quarterly disclosure files covering H-1B, H-1B1, E-3, PERM, H-2A, and H-2B labor certifications. The files are large Excel spreadsheets, released per program and per fiscal year, where column layouts can change between releases. The data is public, but it is published as bulk files, not as a queryable service.

Can I parse the DOL OFLC disclosure files myself?

Yes. The files are free to download from dol.gov. But each program and fiscal year is a separate multi-megabyte Excel file with hundreds of columns, and the schema shifts between quarterly releases — column names and wage fields move, so a parser built for one quarter can break on the next. You also normalize wages yourself, since they are recorded as hourly, weekly, monthly, or yearly with separate unit-of-pay columns.

What is the advantage of a sponsorship API over the DOL Excel files?

A sponsorship API like SponsorLens turns the quarterly Excel dumps into one normalized JSON schema you query by employer, program, occupation, or wage. Offered and prevailing wages are annualized so hourly and salaried roles compare directly, the six programs share one endpoint, and every record carries a source_url back to the original OFLC file. You skip the download-and-parse pipeline entirely.

How do I check if a company sponsors H-1B visas?

Query the certifications by employer name. With SponsorLens that is GET /v1/certifications?employer=nvidia&program=h-1b, which returns the count, roles, worksite states, wages, and case statuses for that employer. Doing it from the raw DOL files means filtering the relevant program and fiscal-year spreadsheets on the employer-name column yourself.