Best Job Posting Data APIs for Labor Market Research – October 2026

Most job posting data APIs will get you a list of open roles. Fewer will give you occupation codes, salary data, and historical depth going back years. Even fewer pair all of that with funding and technology signals so you can ask more precise questions about the same company. This list breaks down who the real options are right now and where each one fits.

TLDR:

• Job posting data APIs give you structured, queryable hiring records, turning workforce activity into analyzable data.
• O*NET occupation codes let you compare hiring across companies and industries without normalizing title formats yourself.
• Pairing job postings with financing or technology signals for the same account makes hiring intent stronger as a buying signal.
• Look for a provider supporting API, flat files, webhooks, and MCP so the data fits your pipeline without rebuilding around it.
• PredictLeads covers 279.7 million-plus historical Job Openings since 2018 and pairs them with Technology Detections, News Events, and Financing Events in one API.

What Is a Job Posting Data API?

A job posting data API gives you programmatic access to structured records of open roles, pulled from company career pages, job boards, and applicant tracking systems. Instead of scraping websites by hand, you query hiring activity the way you would query any other database. Each record typically includes the job title, location, seniority level, posting date, and often salary data and a full description, all tagged with standardized fields you can filter and aggregate.

The value sits in what hundreds of thousands of listings look like together, not in any single listing. One open role for a data analyst tells you almost nothing. A company opening 15 engineering roles in the same quarter, or an entire sector ramping up hiring for supply chain roles, tells you something about job openings data as a growth signal, strategy, or macro conditions before that story shows up anywhere else.

That is why buyers of this data rarely care about job boards in the traditional sense:

• GTM teams use hiring signals in products and CRMs to spot accounts that are expanding and likely to need new tools.
• Investors use job postings as alternative data for early signals of growth at private companies that do not publish earnings.
• Labor economists and workforce analysts use aggregated postings to track sector-level demand, wage trends, and skill shortages across regions.
• Developers building recruiting products or internal scoring models need the raw feed to power all of the above.

For any of that to work, the data needs structure. A job description in plain text, scattered across thousands of career pages in different formats, is not usable at scale. A job posting data API normalizes titles, categorizes roles using standard classification systems, and timestamps when a posting first appeared and when it disappeared. That turns workforce activity into a dataset you can analyze directly, not a pile of web pages you would have to read one by one.

How We Ranked Job Posting Data APIs

We assessed each provider on six dimensions, using publicly available documentation, pricing pages, and product materials instead of hands-on testing.

• Historical depth and coverage: how far back the data goes, and how many companies or websites it spans. A provider with only a few months of history cannot support labor market analytics that depend on year-over-year comparisons.
• Freshness and update cadence: job postings close fast, and a dataset that refreshes weekly will overstate hiring activity for roles that have already been filled, which matters if you are building intent scoring or real time labor analytics.
• Delivery options: some teams need a REST API for real time lookups, while others need flat files for warehouse ingestion or webhooks for triggered workflows. We checked whether each provider supports more than one delivery method, since that determines whether the data fits an existing pipeline or forces you to build around it.
• Signal breadth beyond raw postings: job openings alone are useful, but pairing them with technographic data, firmographic data, or news events lets you ask more precise questions, such as which companies are hiring for a role while also adopting a specific tool. We noted which providers offer this kind of cross dataset context and which are single purpose job feeds.
• Self-serve accessibility: some vendors gate everything behind a sales call and custom pricing, while others let you sign up, get an API key, and start pulling records within minutes

Best Overall Job Posting Data API: PredictLeads

PredictLeads is a B2B company intelligence data provider built around one idea: hiring activity means more when you can see it alongside technology adoption, funding, news, and company relationships in the same query. The Job Openings Dataset covers 279.7 million-plus historical records since 2018, spanning 2.8 million-plus company websites, with 9.9 million active job openings available at any given time. That scale puts PredictLeads ahead of single-purpose job feeds and turns it into a workforce intelligence layer you can build on directly.

The depth shows up in the numbers you can put to work. With 62 million-plus job openings detected last year and 7.3 million-plus last month, you get enough volume to run trend and velocity calculations at the sector, region, or individual company level. Every job opening carries an ONET occupation code, so you can compare hiring across companies and industries using a standard that already exists in labor economics, instead of normalizing a dozen different title taxonomies. The GET /discover/job_openings endpoint lets you query by ONET code and location directly, which is a fast way to find every company hiring for a specific occupation across a whole market.

What sets the dataset apart is what sits next to it. A competitor hiring spike for backend engineers becomes a stronger signal when you can also see that the same company just closed a financing round through the Financing Events dataset, or picked up a new integration partner in the Connections dataset. You can pull Technology Detections, News Events, and job data on the same company record, in the same API, without stitching together separate vendors. Each job opening also includes a source URL, first_seen_at and last_seen_at timestamps, salary data where available, seniority level, job category, and recruiter contact information when listed, so you are working with a full record instead of a bare title.

Delivery matches how your team works. You can query the REST API for real time lookups, pull flat files into Snowflake or BigQuery for warehouse-scale analysis, set up webhooks for triggered workflows, or connect through the MCP server for AI agent use cases. Sign up and you get 100 free API credits with no credit card required, and pay-as-you-go pricing after that starts at a $40 monthly minimum.

Taken together, this is a job posting data API that treats hiring as one signal in a larger intelligence picture instead of a standalone product. For GTM teams, developers, investors, and market intelligence analysts who need hiring data connected to technology, funding, and relationship signals, that combination is hard to replicate by piecing together separate vendors.

Coresignal

Coresignal is a B2B data provider focused on professional network data and job postings at high volume. Its job postings dataset covers 468 million-plus records collected from job boards, company career pages, and professional networks, making it one of the larger raw feeds available. The data is structured around individual job records with title, location, description, and posting dates, and it delivers through flat files for warehouse ingestion.

What They Offer

• Job posting records at 468 million-plus total postings, sourced from job boards, career pages, and professional networks
• Employee and professional profile data alongside job postings, which widens the use case toward workforce sizing and talent intelligence
• Flat file delivery for bulk ingestion into data warehouses
• Custom data packages arranged through a sales process, with no self-serve API access or public pricing

Good for: data teams that need high-volume raw job feeds for workforce analytics or talent intelligence platforms, and are comfortable with a custom contract and flat file delivery.

Limitation: Coresignal does not assign O*NET occupation codes, so you would need to normalize job titles yourself before running any standardized occupation-level comparisons. There are no technographic signals, news event categorization, or financing data in the same dataset, which limits its usefulness as a multi-signal intent layer for GTM workflows. Pricing is custom with no self-serve option, which adds friction for smaller teams or developers reviewing the data before committing.

Bottom line: Coresignal fits teams that need raw posting volume and are building their own normalization layer on top. For GTM teams or developers who need occupation-coded records and cross-dataset context in a single API, PredictLeads covers that without requiring separate vendors or custom title normalization work.

People Data Labs

People Data Labs (PDL) is a data provider built around person-level records. Its core product is a person enrichment API covering over 3 billion professional profiles, and its job postings dataset is an extension of that, sourcing posting data from professional networks, job boards, and company career pages. The primary use case is enrichment: you pass in a name, email, or domain and get back structured profile or company data. Job postings sit alongside that as a secondary signal, not the main event.

What They Offer

• A person enrichment API covering over 3 billion profiles with fields including employment history, education, location, and contact information
• A company enrichment API with firmographic fields including headcount, industry, location, and revenue range
• Job postings data sourced from professional networks and job boards, delivered as structured records with title, location, and description fields
• Self-serve API access with public pricing and a free tier to test the data before committing to a paid plan

Good for: enrichment-focused teams that need person and company profile data alongside job postings, and want self-serve access without a sales process.

Limitation: People Data Labs does not assign O*NET occupation codes, so standardized occupation-level comparisons require your own title normalization. There are no technographic signals, news event categorization, or financing data in the same dataset, which limits its use as a multi-signal intent layer. Job postings are a secondary product alongside person profiles, so the dataset does not carry the historical depth or hiring-specific features, such as first_seen_at and last_seen_at timestamps, salary normalization, and seniority classification, that dedicated job posting APIs provide.

Bottom line: People Data Labs fits teams whose primary need is person and company enrichment, with job postings as a supplementary signal. For teams that need occupation-coded job records, historical depth back to 2018, and the ability to pair hiring data with technology and financing signals in one query, PredictLeads covers that ground more directly.

Crustdata

Crustdata is a B2B data provider focused on real-time company growth signals, with job postings as one of several data types alongside funding events and headcount information. Its primary audience is GTM teams and developers who want to track company expansion signals and trigger outreach on funding or hiring activity. Delivery is through a REST API, and access requires going through a sales process with no self-serve signup option.

What They Offer

• Job postings data paired with headcount signals and funding event data, aimed at identifying companies in a growth phase
• Real-time hiring and funding signals accessible via a REST API, designed for triggering GTM workflows when a company crosses a growth threshold
• Coverage focused on high-growth and venture-backed companies, not the broad universe of employers across all segments
• Custom access and pricing arranged through a sales process, with no self-serve API tier or public pricing page

Good for: GTM teams that primarily want to trigger outreach on a combination of hiring and recent funding signals, and whose target accounts are largely venture-backed or high-growth companies.

Limitation: Crustdata does not assign O*NET occupation codes, so any occupation-level analysis requires building your own title normalization layer. Coverage skews toward funded, high-growth companies and excludes the broader employer universe, which limits its usefulness for labor market analytics or sector-wide hiring trend analysis. There are no technographic signals, news event categorization, or connections data in the same dataset, and pricing is custom with no self-serve option.

Bottom line: Crustdata fits GTM teams whose workflow is narrowly focused on hiring-plus-funding triggers for venture-backed accounts. For teams that need occupation-coded records, broader employer coverage, and the ability to pair hiring data with technology adoption and news signals in one query, PredictLeads covers that ground without requiring separate normalization work or a sales call to get started.

LinkUp

LinkUp sources job postings exclusively from employer career pages, positioning itself as an aggregator-free dataset for labor market research. It has indexed postings directly from employer career pages and applicant tracking systems since 2007, and investment analysts, hedge funds, and labor market researchers rely on it for employer-sourced job data with long historical archives and daily delivery.

What They Offer

• Individual job records carrying the title, full description, location, URL, occupation and sector codes, company identifier, and where applicable the public market ticker
• LinkUp Raw dating back to 2007, including full job postings sourced directly from employer websites along with related statistics and analysis
• An Extracted Salary dataset covering 34 million postings since January 2019 that explicitly stated a USD compensation amount, normalized to annual equivalents
• Data feed delivery via FTP, AWS, Snowflake, or Azure in JSON, CSV, or XML formats

Good for: investment teams joining hiring trends to portfolio data, where the dataset’s embedded ticker symbols make it a direct fit for financial modeling.

Limitation: LinkUp is enterprise-only with no self-serve option and no public pricing. It was also acquired by GlobalData in late 2024, which introduces some uncertainty about product direction going forward. LinkUp covers only job posting data, with no technographic detections, news event categorization, financing signals, or company connections, so GTM and sales teams cannot use it as a multi-signal intent layer the way they might use a broader dataset.

Bottom line: LinkUp covers historical job posting data sourced directly from employer career pages, with ticker symbol tagging suited to financial modeling workflows. For GTM teams and developers who need job signals as part of a workflow that also touches technology adoption, funding, or company relationships, PredictLeads covers that ground more directly.

Sources

• https://brightdata.com/blog/web-data/best-job-apis
• https://www.deweydata.io/data-partners/linkup

Revelio Labs

Revelio Labs takes a different angle than most providers on this list: it treats job postings as raw material for workforce analytics, not as a standalone feed. The company aggregates and standardizes hundreds of millions of public employment records to build a picture of workforce composition and trends for any company, public or private.

What They Offer

• COSMOS, a large job posting database with billions of postings, aggregated and standardized across hundreds of millions of public employment records from job boards, career pages, and professional networks
• Workforce analytics built on top of raw postings, including workforce composition, hiring trend analysis, and headcount tracking by company, industry, and occupation using a proprietary standardized taxonomy
• Coverage of both public and private companies, giving investors and analysts a view of workforce activity at companies that do not publish earnings or headcount figures
• Data delivery through custom arrangements, with pricing and access negotiated through a sales process, not a self-serve tier

Good for: investment analysts, HR-tech teams, and workforce researchers who want hiring trends packaged into finished analytics instead of raw job records, and whose primary need is workforce composition and trend reporting over a queryable job feed.

Limitation: Revelio Labs uses a proprietary occupation taxonomy instead of O*NET codes, so any cross-dataset comparison that relies on a standard occupation classification requires you to map their categories yourself. There are no technographic signals, news event categorization, or financing data in the same dataset, which rules it out as a multi-signal intent layer for GTM workflows. Access is custom with no self-serve option and no public pricing, which adds friction for developers or smaller teams vetting the data before committing.

Bottom line: Revelio Labs fits teams whose primary need is packaged workforce analytics and trend reporting, particularly for investment research or HR-tech use cases. For teams that need occupation-coded raw records, historical depth back to 2018, and the ability to pair hiring data with technology adoption and financing signals in one query, PredictLeads covers that ground without requiring a custom contract or separate normalization work.

Feature Comparison Table of Job Posting Data APIs

Feature	PredictLeads	Coresignal	People Data Labs	Crustdata	LinkUp	Revelio Labs
Historical job data depth	Since 2018, 279.7M+ records	468M+ job postings	Structured global postings	Real-time, growth-focused	Since 2007	Hundreds of millions of records
O*NET or standardized occupation codes	Yes	No	No	No	Yes (occupation and sector codes)	No, uses proprietary taxonomy
Technographic signals in same API	Yes	No	No	No	No	No
News event signals in same API	Yes	No	No	Yes, funding signals only	No	No
Financing signals in same API	Yes	No	No	Yes	No	No
Self-serve API with public pricing	Yes	Custom pricing	Yes	Custom pricing	No, enterprise only	Custom pricing
Flat files and webhooks	Yes	Flat files, no webhooks	Flat files, no webhooks noted	No	Flat files, no webhooks	Custom delivery, no webhooks noted

A few patterns stand out here. PredictLeads is the only provider on this list combining standardized occupation codes with technographic, news, and financing signals inside the same API, which matters if you want hiring data to inform more than a single workflow. Coresignal and Revelio Labs lean toward volume and workforce analytics instead of multi-signal breadth. Crustdata pairs job signals with funding data but skips occupation coding entirely. LinkUp remains the strongest option for employer-sourced historical depth, though its enterprise-only access limits how quickly a smaller team can get started.

Why PredictLeads Is the Best Job Posting Data API

Volume alone does not make hiring data useful. Context does, and PredictLeads is the only provider on this list that pairs Job Openings with Technology Detections, News Events, Financing Events, Connections, and Website Evolution inside one API, with O*NET classification and source-linked records built in from the start.

That structure changes who can use the data and how:

• A GTM team gets hiring and tech B2B sales signals alongside funding signals for the same account, so a spike in engineering job postings can be checked against recent financing activity before a rep ever reaches out.
• A developer gets one schema to integrate instead of five, which cuts the maintenance load that comes with B2B data enrichment for hiring and funding.
• An investor gets hiring velocity next to financing history in the same record, useful for tracking a portfolio company’s build-out pace without cross-referencing outside sources.
• A market intelligence analyst gets sector-level hiring trends tied to news and product activity in a single query, which shortens the research cycle for competitor and sector tracking.

Add flexible delivery through API, flat files, webhooks, and MCP, and PredictLeads becomes the integration point where a fragmented list of point solutions would otherwise be required.

Frequently Asked Questions

What is the difference between a job posting data API and a workforce analytics tool?

A job posting data API gives you raw, structured hiring records so you can build your own analysis, scoring model, or dashboard. A workforce analytics tool takes that same underlying data and packages it into finished reports or visualizations for you. If you need to combine hiring data with other signals like funding or tech adoption, you want the API, not a closed reporting layer.

How does O*NET classification help with labor market analytics?

ONET (Occupational Information Network) codes give every job posting a standardized occupation label, so you can compare hiring across companies and industries without normalizing dozens of different title formats yourself. Without it, you would need to manually map titles like “software engineer,” “SWE,” and “backend developer” into one category before you could run any sector level comparison. With ONET built into the data, you can filter and aggregate by occupation from the first query.

Can job posting data be used as a buying signal for sales teams?

Yes. When you see a company posting for roles that align with your product, such as a surge in sales development representative (SDR) hiring before a new SDR tool purchase, you have a timing signal for outreach that public firmographic data alone will not give you. Pairing job postings with financing or technology signals for the same account makes that signal stronger.

What delivery options should I look for in a job posting data API?

Look for a provider that supports the right API, flat files, and webhooks delivery for your use case, since your needs will likely span more than one over time. If you are building AI agent workflows, also check whether the provider offers a Model Context Protocol (MCP) server so your agents can query the data conversationally.

Final thoughts on Job Posting Data APIs and Workforce Intelligence

Hiring data works best when it answers a specific question, not when it sits in a spreadsheet waiting for one. The providers here cover different ground, so the fit depends on your use case, your team’s technical setup, and how many other signals you need alongside job openings. Get started for free and see what the data looks like before committing to anything.

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FAQ

How do I choose the right job posting data API from options like PredictLeads, LinkUp, Coresignal, and Revelio Labs?

Start with your primary use case. If you need hiring data connected to technology adoption, funding, and company relationships for GTM or investment workflows, PredictLeads is the only option on this list that combines all of those signals in one API. If you need employer-sourced historical depth tied to ticker symbols for financial modeling, LinkUp fits that specific need. If you want workforce analytics packaged into reports instead of raw records, Revelio Labs is worth considering.

Is PredictLeads better than LinkUp for GTM and sales workflows?

Yes, for GTM purposes. LinkUp covers job postings only, with no technographic signals, news events, or financing data, and it requires an enterprise contract with no self-serve access. PredictLeads lets a GTM team combine hiring intent with tech stack signals and funding activity on the same company record, which gives you stronger timing context before outreach.

When should I choose Crustdata or Coresignal over PredictLeads for job posting data?

Crustdata pairs job signals with funding data but skips O*NET occupation coding, which limits its use in labor market analytics requiring standardized role comparisons. Coresignal offers high volume at 468 million-plus postings but no cross-dataset signals. Either could fit if you need raw job feed volume and have no requirement for occupation-standardized records or multi-signal context, though both use custom pricing with no self-serve API access.

What should I look for in a job posting data API if I’m building a scoring model or AI agent?

Look for O*NET-coded records so your model has a consistent occupation taxonomy to work with, plus first_seen_at and last_seen_at timestamps for velocity calculations. For AI agent workflows, check whether the provider offers a Model Context Protocol (MCP) server so agents can query the data without custom integration work. PredictLeads covers all three and also supports flat files for warehouse ingestion and webhooks for triggered workflows.

Is People Data Labs or PredictLeads better for labor market analytics?

PredictLeads is better suited for labor market analytics that depend on standardized occupation codes and historical depth, since every job opening carries an ONET code and the dataset goes back to 2018 with 279.7 million-plus records. People Data Labs offers structured global postings but does not include ONET classification or cross-dataset signals like financing and technographic data, which limits how precisely you can segment hiring trends by occupation across industries.