{"id":877,"date":"2026-04-29T08:22:46","date_gmt":"2026-04-29T08:22:46","guid":{"rendered":"https:\/\/blog.predictleads.com\/?p=877"},"modified":"2026-11-02T20:05:08","modified_gmt":"2026-11-02T20:05:08","slug":"top-5-job-data-providers","status":"publish","type":"post","link":"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/","title":{"rendered":"Top 5 Job Data Providers in September 2026"},"content":{"rendered":"<p class=\"byline\"><em>By Robert Fon \/ October 21, 2026 (originally published April 29, 2026)<\/em><\/p>\n\n<p>The best job data providers in October 2026 are PredictLeads, Coresignal, Bright Data, LinkUp, and Revelio Labs, with PredictLeads ranking first because it pairs Job Openings with Technology Detections and Financing Events in one API.<\/p><div id=\"ez-toc-container\" class=\"ez-toc-v2_0_89 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"><\/path><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class=\"ez-toc-list ez-toc-list-level-1 \"><ul class=\"ez-toc-list-level-3\"><li class=\"ez-toc-heading-level-3\"><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#TLDR\">TLDR:<\/a><\/li><\/ul><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#What_Is_a_Job_Data_Provider\">What Is a Job Data Provider?<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#How_We_Ranked_These_Tools\">How We Ranked These Tools<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Best_Overall_Job_Data_Provider_PredictLeads\">Best Overall Job Data Provider: PredictLeads<\/a><ul class=\"ez-toc-list-level-3\"><li class=\"ez-toc-heading-level-3\"><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Why_the_Combination_Wins\">Why the Combination Wins<\/a><\/li><\/ul><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Coresignal\">Coresignal<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Bright_Data\">Bright Data<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#LinkUp\">LinkUp<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Revelio_Labs\">Revelio Labs<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Job_Data_Providers_Comparison_Table_October_2026\">Job Data Providers Comparison Table (October 2026)<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Why_PredictLeads_Is_the_Best_Job_Data_Provider\">Why PredictLeads Is the Best Job Data Provider<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Frequently_Asked_Questions\">Frequently Asked Questions<\/a><ul class=\"ez-toc-list-level-3\"><li class=\"ez-toc-heading-level-3\"><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#What_is_a_job_data_provider\">What is a job data provider?<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-3\"><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Which_job_data_provider_has_the_most_job_postings_in_2026\">Which job data provider has the most job postings in 2026?<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-3\"><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Is_PredictLeads_better_than_Coresignal_for_job_data\">Is PredictLeads better than Coresignal for job data?<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-3\"><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Can_job_openings_data_be_combined_with_technographic_data_in_one_API\">Can job openings data be combined with technographic data in one API?<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-3\"><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#How_often_is_PredictLeads_job_openings_data_refreshed\">How often is PredictLeads job openings data refreshed?<\/a><\/li><\/ul><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Ready_to_see_this_in_your_own_data\">Ready to see this in your own data?<\/a><\/li><li class=\"ez-toc-page-1 ez-toc-heading-level-2\"><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/#Final_Thoughts_on_Job_Data_Providers\">Final Thoughts on Job Data Providers<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"TLDR\"><\/span><span class=\"ez-toc-section\" id=\"TLDR\"><\/span>TLDR:<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul class=\"list-disc pl-6\"><li class=\"list-item\">A job data provider turns raw job postings into structured, company-linked records; the best ones add occupation codes, seniority, and salary fields instead of raw text.<\/li><li class=\"list-item\">Most providers in this comparison treat jobs as a single, standalone dataset; none except PredictLeads pairs job openings with technology detections and financing events in the same API.<\/li><li class=\"list-item\">Published coverage numbers vary widely by vendor and are rarely audited independently, so treat any single provider\u2019s record count as a starting point, not a final scorecard entry.<\/li><li class=\"list-item\">Delivery method matters as much as volume: API access suits real-time enrichment, flat files suit a data warehouse backfill, and webhooks and Model Context Protocol (MCP) suit trigger-based and AI-agent workflows.<\/li><li class=\"list-item\">PredictLeads tracks 300M+ Job Openings records since 2016 across 3M+ companies, with 9.8M+ active openings and 665,500+ companies currently hiring at any given time.<\/li><\/ul><h2><span class=\"ez-toc-section\" id=\"What_Is_a_Job_Data_Provider\"><\/span><span class=\"ez-toc-section\" id=\"What_Is_a_Job_Data_Provider\"><\/span>What Is a Job Data Provider?<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A job data provider is a company that collects, structures, and delivers job posting records, typically sourced from employer websites, career pages, job boards, or applicant tracking systems, so other businesses can analyze hiring activity without scraping it themselves. The category breaks down into a few recognizable sub-types:<\/p>\n<ul class=\"list-disc pl-6\"><li class=\"list-item\"><strong>Raw job postings:<\/strong> the posting text itself (title, description, location), often deduplicated across the same opening appearing on multiple boards.<\/li><li class=\"list-item\"><strong>Structured job fields:<\/strong> normalized attributes pulled out of that text, such as occupation code, seniority level, salary range, and contract type.<\/li><li class=\"list-item\"><strong>Hiring trend data:<\/strong> aggregated counts over time (headcount change, open-role volume by category) used for labor-market or workforce analysis.<\/li><li class=\"list-item\"><strong>Company-linked hiring data:<\/strong> job postings tied back to a specific company record, so a single opening becomes part of a broader account profile instead of an isolated listing.<\/li><\/ul><p>The dimension that separates these datasets commercially is not posting volume so much as what a job opening connects to once it is structured, which is the basis for the ranking below. PredictLeads classifies every job opening into one of 26 categories using O*NET occupation codes, which is the normalization layer most providers in this space leave for the buyer to build. For a broader look at applying this data across a GTM motion, see the <a href=\"https:\/\/predictleads.com\/blog\/job-openings-data-hiring-signals-b2b\/\">Job Openings data guide for B2B teams<\/a>.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"Why_Job_Data_Providers_Matter_for_GTM_Teams_in_Late_2026\"><\/span>Why Job Data Providers Matter for GTM Teams in Late 2026<span class=\"ez-toc-section-end\"><\/span><\/h2><p>You are dealing with hiring volume high enough that raw postings alone create noise instead of signal. PredictLeads detected 7M+ job openings last month and 56.7M+ in the last year, so a single posting is easy to find but hard to act on without context. The value now comes from linking a posting to a company record and checking it against other signals, such as evidence of a recent technology change or a funding event, which is what turns <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/blog\/hiring-as-a-buying-signal\/\">hiring as a buying signal<\/a> into something you can act on instead of just another row in a spreadsheet.<\/p><p>GTM teams, data teams building AI agents over Model Context Protocol (MCP), and market intelligence analysts all read the same posting differently, but they judge providers on the same two things: how fresh the data is and how it gets delivered. A provider that refreshes once a week and only ships CSV files will not support an AI agent that needs to query live data or a sales rep who needs to reach out while a hiring spike is still recent.<\/p><h2><span class=\"ez-toc-section\" id=\"How_We_Ranked_These_Tools\"><\/span><span class=\"ez-toc-section\" id=\"How_We_Ranked_These_Tools\"><\/span>How We Ranked These Tools<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>We scored each provider on six dimensions, pulled from public documentation, product pages, and neutral third-party profiles, not hands-on testing of every vendor\u2019s console. Where a provider does not publish a figure, we say so directly instead of guessing at a number that could be wrong in either direction. These are the same dimensions that separate the best job posting data providers from a single scraped dataset:<\/p><ul class=\"list-disc pl-6\"><li class=\"list-item\"><strong>Coverage scale:<\/strong> how many companies, websites, or postings the provider claims to track, and whether that claim is independently verifiable.<\/li><li class=\"list-item\"><strong>Historical depth:<\/strong> how far back the dataset goes, which determines whether you can build trendlines or only see a snapshot.<\/li><li class=\"list-item\"><strong>Occupation normalization:<\/strong> whether postings are mapped to a standard taxonomy, such as O*NET, or left as raw title strings.<\/li><li class=\"list-item\"><strong>Delivery methods:<\/strong> the technical options for getting the data out: API, flat files, webhooks, or an AI-agent interface like MCP.<\/li><li class=\"list-item\"><strong>Source-level transparency:<\/strong> whether you can trace a record back to the specific page, job board, or filing it came from.<\/li><li class=\"list-item\"><strong>Signal pairing:<\/strong> whether job data can be combined with other company signals, such as technology detections or financing events, inside the same product.<\/li><\/ul><p>These six dimensions structure both the comparison table further down and the sections that follow.<\/p>\n\n<table class=\"border-collapse table-fixed w-full max-w-full\" style=\"border-collapse: collapse; width: 100%; min-width: 150px\"><caption style=\"caption-side: top; text-align: left; color: #000000; padding: 0 0 8px; font-size: 14px\">Questions to Ask Any Job Data Provider<\/caption><tbody><tr class=\"\"><th colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #f9fafb; color: #000000; padding: 12px; text-align: left; font-size: 14px\"><p>Dimension<\/p><\/th><th colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #f9fafb; color: #000000; padding: 12px; text-align: left; font-size: 14px\"><p>Question to ask the vendor<\/p><\/th><th colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #f9fafb; color: #000000; padding: 12px; text-align: left; font-size: 14px\"><p>Why it matters<\/p><\/th><\/tr><tr class=\"\"><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Coverage scale<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>How do you count a record, and can you point to a source that checks your numbers?<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Vendors define company and posting counts differently, so a raw figure alone can mislead.<\/p><\/td><\/tr><tr class=\"\"><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Historical depth<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>How far back does your archive go, and is older data still queryable?<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>A short history limits trendline analysis to a snapshot in time.<\/p><\/td><\/tr><tr class=\"\"><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Occupation normalization<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Do you map titles to a standard taxonomy, or hand back raw title strings?<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Without normalization, you build your own classification layer before the data is usable.<\/p><\/td><\/tr><tr class=\"\"><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Delivery methods<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Which of API, flat files, webhooks, or MCP do you support?<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Your pipeline and tooling determine which delivery format fits without extra engineering work.<\/p><\/td><\/tr><tr class=\"\"><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Source-level transparency<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Can I trace a record back to the page or filing it came from?<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Traceable sources let you validate a signal instead of taking it on faith.<\/p><\/td><\/tr><tr class=\"\"><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Signal pairing<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Can job data link to technology detections or financing events in the same product?<\/p><\/td><td colspan=\"1\" rowspan=\"1\" style=\"border: 1px solid #d1d5db; background-color: #ffffff; color: #000000; padding: 12px; font-size: 14px\"><p>Paired signals turn a single posting into a reason to reach out now.<\/p><\/td><\/tr><\/tbody><\/table><h2><span class=\"ez-toc-section\" id=\"Best_Overall_Job_Data_Provider_PredictLeads\"><\/span><span class=\"ez-toc-section\" id=\"Best_Overall_Job_Data_Provider_PredictLeads\"><\/span>Best Overall Job Data Provider: PredictLeads<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul class=\"list-disc pl-6\"><li class=\"list-item\"><strong>Job Openings:<\/strong> 300M+ historical records since 2016 across 3M+ companies, with 9.8M+ active openings at any time and 665,500+ companies currently hiring, each normalized to O*NET occupation codes across 26 job categories and five seniority tiers.<\/li><li class=\"list-item\"><strong>Technology Detections:<\/strong> 1.96B+ detections since 2018 across 98M+ company domains, built from five sources (website script tags, DNS records, IP ranges, cookies, and job descriptions), which lets you pair a hiring surge with evidence of which technologies a company uses or has recently used.<\/li><li class=\"list-item\"><strong>Financing Events:<\/strong> 216,700+ events since 2016, so a hiring spike can be checked against a recent funding round on the same account record.<\/li><li class=\"list-item\"><strong>Companies:<\/strong> 133M+ companies tracked across 195 countries, the firmographic anchor every job opening, detection, and event ties back to, useful for <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/blog\/define-top-accounts-with-data\/\">defining your top accounts<\/a> before you plan outreach.<\/li><\/ul><figure>\n<svg viewBox=\"0 0 1000 220\" role=\"img\" aria-label=\"PredictLeads Job Openings dataset by the numbers: 279.2 million plus records since 2018, 2.9 million plus websites with job openings tracked, 10.2 million plus active job openings at any time, and 710,600 plus companies currently hiring\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"width:100%;height:auto;font-family:'Poppins','Segoe UI',system-ui,sans-serif;background:#f3ecf8;border-radius:12px;\">\n<rect x=\"0\" y=\"0\" width=\"1000\" height=\"220\" fill=\"#f3ecf8\"><\/rect>\n<g>\n<rect x=\"30\" y=\"30\" width=\"220\" height=\"160\" rx=\"16\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"><\/rect>\n<text x=\"140\" y=\"95\" text-anchor=\"middle\" font-size=\"34\" font-weight=\"800\" fill=\"#76508e\">279.2M+<\/text>\n<text x=\"140\" y=\"128\" text-anchor=\"middle\" font-size=\"14\" fill=\"#3a2f4d\">Job Openings records<\/text>\n<text x=\"140\" y=\"148\" text-anchor=\"middle\" font-size=\"14\" fill=\"#3a2f4d\">since 2018<\/text>\n<\/g>\n<g>\n<rect x=\"270\" y=\"30\" width=\"220\" height=\"160\" rx=\"16\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"><\/rect>\n<text x=\"380\" y=\"95\" text-anchor=\"middle\" font-size=\"34\" font-weight=\"800\" fill=\"#76508e\">2.9M+<\/text>\n<text x=\"380\" y=\"128\" text-anchor=\"middle\" font-size=\"14\" fill=\"#3a2f4d\">websites with job<\/text>\n<text x=\"380\" y=\"148\" text-anchor=\"middle\" font-size=\"14\" fill=\"#3a2f4d\">openings tracked<\/text>\n<\/g>\n<g>\n<rect x=\"510\" y=\"30\" width=\"220\" height=\"160\" rx=\"16\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"><\/rect>\n<text x=\"620\" y=\"95\" text-anchor=\"middle\" font-size=\"34\" font-weight=\"800\" fill=\"#76508e\">10.2M+<\/text>\n<text x=\"620\" y=\"128\" text-anchor=\"middle\" font-size=\"14\" fill=\"#3a2f4d\">active job openings<\/text>\n<text x=\"620\" y=\"148\" text-anchor=\"middle\" font-size=\"14\" fill=\"#3a2f4d\">at any time<\/text>\n<\/g>\n<g>\n<rect x=\"750\" y=\"30\" width=\"220\" height=\"160\" rx=\"16\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"><\/rect>\n<text x=\"860\" y=\"95\" text-anchor=\"middle\" font-size=\"32\" font-weight=\"800\" fill=\"#76508e\">710,600+<\/text>\n<text x=\"860\" y=\"128\" text-anchor=\"middle\" font-size=\"14\" fill=\"#3a2f4d\">companies currently<\/text>\n<text x=\"860\" y=\"148\" text-anchor=\"middle\" font-size=\"14\" fill=\"#3a2f4d\">hiring<\/text>\n<\/g>\n<\/svg>\n<figcaption style=\"color:#3a2f4d;font-size:13px;\">PredictLeads Job Openings dataset, by the numbers (October 2026).<\/figcaption>\n<\/figure>\n\n<h3><span class=\"ez-toc-section\" id=\"Why_the_Combination_Wins\"><\/span><span class=\"ez-toc-section\" id=\"Why_the_Combination_Wins\"><\/span>Why the Combination Wins<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>PredictLeads is the only provider in this comparison that natively combines job postings, technology detections, financing events, and firmographic company data in a single API. A job data provider alone tells you a company is hiring. Pairing that with evidence of which tools a company\u2019s site or postings reference, plus a recent financing event, tells you whether that hiring ties to a specific initiative worth a tailored pitch, not a generic list add.<\/p><figure class=\"wp-block-image\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/8RyHuBfkHxjbjNxm6MB7u.png\" alt=\"Clean, modern flat diagram on a light background. Four labeled input blocks on the left: &quot;Job Openings&quot;, &quot;Technology Detections&quot;, &quot;Financing Events&quot;, &quot;Company Records&quot;. Arrows from each block converge into one central card labeled &quot;Account Signal&quot;. To the right of that card, four small delivery icons labeled &quot;API&quot;, &quot;Flat Files&quot;, &quot;Webhooks&quot;, &quot;MCP&quot;. Minimal B2B data style, blue and slate palette, crisp sans-serif labels, no other text.\" class=\"wp-image-2396\" srcset=\"https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/8RyHuBfkHxjbjNxm6MB7u.png 1672w, https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/8RyHuBfkHxjbjNxm6MB7u-300x169.png 300w, https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/8RyHuBfkHxjbjNxm6MB7u-1024x576.png 1024w, https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/8RyHuBfkHxjbjNxm6MB7u-768x432.png 768w, https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/8RyHuBfkHxjbjNxm6MB7u-1536x864.png 1536w\" sizes=\"(max-width: 1672px) 100vw, 1672px\" \/><\/figure><p>Delivery runs through API, flat files, webhooks, and MCP, so a data team can pull the same signal stack whether it is building a real-time enrichment call, a warehouse backfill, or an AI agent query. For the full dataset schema, see the <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/job_listings\">PredictLeads Job Openings dataset page<\/a>. For the mechanics behind a single posting becoming a company growth signal, see how <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/blog\/job-openings-data-company-growth\/\">job openings data as a growth indicator<\/a> works or the full <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/blog\/predictleads-job-openings-dataset-sales-growth\/\">PredictLeads Job Openings dataset overview<\/a>.<\/p><h2><span class=\"ez-toc-section\" id=\"Coresignal\"><\/span><span class=\"ez-toc-section\" id=\"Coresignal\"><\/span>Coresignal<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Coresignal is a public web data provider that sells structured datasets for jobs, companies, and employee profiles, built for engineering teams that want to run their own enrichment or AI pipelines.<\/p>\n<p><strong>What They Offer:<\/strong><\/p>\n<ul class=\"list-disc pl-6\"><li class=\"list-item\">Job Postings Data, Company Data, and Employee Data sold as separate dataset products, each available as API access or bulk database files.<\/li><li class=\"list-item\">Delivery formats including JSON, JSONL, CSV, and Parquet, aimed at teams loading data directly into their own warehouse or pipeline.<\/li><li class=\"list-item\">A neutral third-party profile lists Coresignal\u2019s job postings dataset at 468M+ records and its multi-source company data at 70M+ records, though other public sources cite different totals for the same datasets.<\/li><\/ul><p><strong>Good for:<\/strong> engineering teams that want a large, developer-first jobs dataset they can parse and structure themselves, without needing a packaged, ready-to-query API layer.<\/p>\n<p><strong>Limitation:<\/strong> Coresignal\u2019s public materials describe job, company, and employee datasets, but do not list a dedicated technographic detection dataset or a financing-events dataset in the same product, so pairing a hiring signal with stack or funding context means adding a second and third data source.<\/p>\n<p><strong>Bottom line:<\/strong> PredictLeads pairs 300M+ Job Openings records with 1.96B+ Technology Detections and 216,700+ Financing Events in one API; Coresignal&#8217;s public profile focuses on job, company, and employee datasets without a published technographic layer.<\/p><p><strong>Sources:<\/strong> <a href=\"https:\/\/datarade.ai\/data-providers\/coresignal\/profile\">Coresignal\u2019s profile on Datarade<\/a>.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"Bright_Data\"><\/span><span class=\"ez-toc-section\" id=\"Bright_Data\"><\/span>Bright Data<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Bright Data is a web-data infrastructure company known for proxy networks and a dataset marketplace, which includes a dedicated Jobs dataset and a Jobs Scraper API alongside dozens of other data categories.<\/p>\n<p><strong>What They Offer:<\/strong><\/p>\n<ul class=\"list-disc pl-6\"><li class=\"list-item\">A Jobs dataset and Jobs Scraper API sold through Bright Data\u2019s broader data-collection service, priced per record or per request instead of as a flat hiring-data subscription.<\/li><li class=\"list-item\">A wider catalog of pre-collected and on-demand datasets spanning e-commerce, social media, and real estate in addition to jobs.<\/li><li class=\"list-item\">G2 reviewers rate Bright Data at 4.7 out of 5 across 349 reviews, with academic and research users praising the dataset catalog\u2019s ease of use.<\/li><\/ul><p><strong>Good for:<\/strong> teams that already use Bright Data\u2019s scraping infrastructure for other projects and want to add custom, on-demand job-listing collection from sites a packaged dataset does not cover.<\/p>\n<p><strong>Limitation:<\/strong> Jobs data is one category inside a general-purpose scraping and proxy business, not a dedicated hiring-intelligence product, so there is no published occupation normalization, such as O*NET codes, or company-level hiring-trend layer built into the jobs offering.<\/p><p><strong>Bottom line:<\/strong> PredictLeads normalizes every job opening to O*NET occupation codes and seniority levels across 3M+ companies; Bright Data\u2019s jobs output is closer to a scraping feed that still needs structuring downstream.<\/p><p><strong>Sources:<\/strong> <a href=\"https:\/\/www.g2.com\/products\/bright-data\/reviews\">Bright Data\u2019s reviews on G2<\/a>.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"LinkUp\"><\/span><span class=\"ez-toc-section\" id=\"LinkUp\"><\/span>LinkUp<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>LinkUp is a labor-market data provider that sources its job postings directly from employer career pages instead of job boards, with a historical focus on public company hiring trends.<\/p><p><strong>What They Offer:<\/strong><\/p>\n<ul class=\"list-disc pl-6\"><li class=\"list-item\">LinkUp RAW, a job postings dataset collected directly from employer websites with history back to 2007, per its public data profile.<\/li><li class=\"list-item\">Delivery through data marketplaces including Snowflake Data Marketplace, AWS Data Exchange, and FactSet Marketplace instead of a standalone developer API.<\/li><li class=\"list-item\">A neutral third-party profile lists LinkUp\u2019s coverage at approximately 200 million historical job postings and 60,000 public and private companies, with roughly 5 million daily active jobs.<\/li><\/ul><p><strong>Good for:<\/strong> economists and labor-market researchers who want postings sourced directly from employer sites for macro hiring trend analysis, particularly across large public companies. If you need company-linked signals beyond postings, compare <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/blog\/linkup-alternatives\/\">LinkUp alternatives<\/a>. For a broader rundown of options, see this overview of <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/blog\/job-posting-data-apis-labor-analytics\/\">job posting data APIs for labor research<\/a>.<\/p><p><strong>Limitation:<\/strong> the published company count on LinkUp\u2019s neutral profile, around 60,000, is a fraction of PredictLeads\u2019 3M+ companies with job openings, and the public listing does not show a technographic or financing-event layer to pair hiring with other buying signals.<\/p><p><strong>Bottom line:<\/strong> PredictLeads tracks 665,500+ companies currently hiring out of a 133M+ company base; LinkUp\u2019s published company coverage focuses on a smaller, more market-research-focused set of mostly larger employers.<\/p><p><strong>Sources:<\/strong> <a href=\"https:\/\/datarade.ai\/data-providers\/linkup\/profile\">LinkUp\u2019s profile on Datarade<\/a>.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"Revelio_Labs\"><\/span><span class=\"ez-toc-section\" id=\"Revelio_Labs\"><\/span>Revelio Labs<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Revelio Labs is a workforce analytics provider that turns public employment records into labor-market and organizational data, built for investment research and HR benchmarking instead of outbound prospecting.<\/p><p><strong>What They Offer:<\/strong><\/p>\n<ul class=\"list-disc pl-6\"><li class=\"list-item\">Aggregated workforce datasets covering hiring trends, attrition, compensation benchmarks, headcount changes, and organizational structure, per its public product description.<\/li><li class=\"list-item\">A customer base that includes hedge funds and institutional investors using workforce data as an alternative-data signal for equity research, alongside HR teams and government organizations.<\/li><li class=\"list-item\">No published API documentation or self-serve delivery option on its neutral third-party profiles, suggesting delivery is handled through direct data licensing.<\/li><\/ul><p><strong>Good for:<\/strong> investment research teams that want workforce analytics (attrition, compensation benchmarking, org structure) as an alternative-data signal, instead of a raw feed of individual job postings to action in a CRM.<\/p><p><strong>Limitation:<\/strong> Revelio Labs has not published dataset scale figures, review counts, or pricing on its neutral G2 or Datarade profiles, which makes it difficult to benchmark its coverage against a provider that names exact figures.<\/p>\n<p><strong>Bottom line:<\/strong> PredictLeads publishes exact, verifiable scale numbers, 300M+ Job Openings records and 133M+ companies tracked, while Revelio Labs\u2019 public profiles list no equivalent figures to compare against.<\/p><p><strong>Sources:<\/strong> <a href=\"https:\/\/www.g2.com\/products\/revelio-labs\/reviews\">Revelio Labs\u2019 profile on G2<\/a>.<\/p>\n\n<h2><span class=\"ez-toc-section\" id=\"Job_Data_Providers_Comparison_Table_October_2026\"><\/span><span class=\"ez-toc-section\" id=\"Job_Data_Providers_Comparison_Table_October_2026\"><\/span>Job Data Providers Comparison Table (October 2026)<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The table below lines up all five providers against the same six dimensions used to build this ranking.<\/p>\n<div style=\"overflow-x:auto;\">\n<table style=\"width:100%;border-collapse:collapse;font-size:14px;\">\n<thead>\n<tr style=\"background:#76508e;color:#ffffff;\">\n<th style=\"padding:10px;text-align:left;border:1px solid #ece0f5;\">Provider<\/th>\n<th style=\"padding:10px;text-align:left;border:1px solid #ece0f5;\">Coverage Scale<\/th>\n<th style=\"padding:10px;text-align:left;border:1px solid #ece0f5;\">Historical Depth<\/th>\n<th style=\"padding:10px;text-align:left;border:1px solid #ece0f5;\">Occupation Normalization<\/th>\n<th style=\"padding:10px;text-align:left;border:1px solid #ece0f5;\">Delivery Methods<\/th>\n<th style=\"padding:10px;text-align:left;border:1px solid #ece0f5;\">Source Transparency<\/th>\n<th style=\"padding:10px;text-align:left;border:1px solid #ece0f5;\">Paired Signals<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background:#ead9f2;\">\n<td style=\"padding:10px;border:1px solid #ece0f5;\"><strong>PredictLeads<\/strong><\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Yes, 279.2M+ records, 2.9M+ websites<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Since 2018<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Yes, O*NET codes, 26 categories<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">API, flat files, webhooks, MCP<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Yes, first_seen_at\/last_seen_at plus source URL<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Yes, 2B+ Technology Detections, 210,800+ Financing Events<\/td>\n<\/tr>\n<tr style=\"background:#ffffff;\">\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Coresignal<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">468M+ job postings (per Datarade)<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Not published for job postings specifically<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Partial, standardized fields, not O*NET<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">API, bulk files (JSON, JSONL, CSV, Parquet)<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Partial, sourced from job boards, ATS, and websites<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">No dedicated technographic or financing dataset<\/td>\n<\/tr>\n<tr style=\"background:#f3ecf8;\">\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Bright Data<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Not published (per-record\/per-request pricing)<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Not published<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">No<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">API, Scraper API, bulk files<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Partial, source depends on scrape configuration<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">No<\/td>\n<\/tr>\n<tr style=\"background:#ffffff;\">\n<td style=\"padding:10px;border:1px solid #ece0f5;\">LinkUp<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">200M+ postings, 60,000 companies (per Datarade)<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Since 2007<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">No<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Data marketplace delivery (Snowflake, AWS Data Exchange, FactSet)<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Partial, employer-website sourced<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">No<\/td>\n<\/tr>\n<tr style=\"background:#f3ecf8;\">\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Revelio Labs<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Not published<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Not published<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">No, workforce analytics categories, not O*NET<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Not published, licensed data<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">Not published<\/td>\n<td style=\"padding:10px;border:1px solid #ece0f5;\">No<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n<h2><span class=\"ez-toc-section\" id=\"Why_PredictLeads_Is_the_Best_Job_Data_Provider\"><\/span><span class=\"ez-toc-section\" id=\"Why_PredictLeads_Is_the_Best_Job_Data_Provider\"><\/span>Why PredictLeads Is the Best Job Data Provider<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<figure class=\"wp-block-image\"><img decoding=\"async\" width=\"1672\" height=\"941\" src=\"https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/sexjXD5uYZNr8C1fz78HU.png\" alt=\"Clean, modern flat decision-guide diagram on a light background for a B2B data article. Five labeled columns for job data providers: &quot;PredictLeads&quot;, &quot;Coresignal&quot;, &quot;Bright Data&quot;, &quot;LinkUp&quot;, &quot;Revelio Labs&quot;. Under each, a short best-fit label: &quot;Paired signal stack (jobs + tech + funding)&quot;, &quot;Raw datasets to structure yourself&quot;, &quot;Custom on-demand scraping&quot;, &quot;Macro labor trends&quot;, &quot;Workforce research&quot;. Minimal style, blue and slate palette, crisp sans-serif labels, no other text.\" class=\"wp-image-2397\" srcset=\"https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/sexjXD5uYZNr8C1fz78HU.png 1672w, https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/sexjXD5uYZNr8C1fz78HU-300x169.png 300w, https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/sexjXD5uYZNr8C1fz78HU-1024x576.png 1024w, https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/sexjXD5uYZNr8C1fz78HU-768x432.png 768w, https:\/\/predictleads.com\/blog\/wp-content\/uploads\/2026\/10\/sexjXD5uYZNr8C1fz78HU-1536x864.png 1536w\" sizes=\"(max-width: 1672px) 100vw, 1672px\" \/><\/figure><p>The providers above each do one piece of the job data problem well. Coresignal structures job, company, and employee records for engineering teams. Bright Data scrapes listings on demand. LinkUp sources postings straight from employer career pages for labor economists. Revelio Labs turns workforce records into investment research. None of them, based on their own public materials, pairs job postings with a technographic layer, a financing-events layer, and a 133M+ company firmographic base in the same product.<\/p><p>So which job data provider should you choose? PredictLeads is the strongest fit when you need hiring data tied to Technology Detections, Financing Events, and company records through API, flat files, webhooks, or MCP. Coresignal fits engineering teams that want raw job, company, and employee datasets they can structure themselves. Bright Data fits teams that need custom, on-demand scraping alongside other data categories. LinkUp fits labor economists who want postings sourced directly from employer career pages through data marketplaces. Revelio Labs fits investment research teams that want workforce analytics such as attrition and compensation benchmarking. No single provider on this list is the right choice for every team, so match the pick to whether you need a paired signal stack, a raw dataset to build on, custom scraping, macro labor trends, or workforce research.<\/p><p>That combination is what lets a hiring signal become a top-of-list account instead of a line item. A company posting five sales roles is a data point. Picture the same company posting five sales roles two months after a Series B, while its Technology Detections show a CRM first seen in the last quarter: that combination can indicate a stronger buying window worth <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/blog\/time-outreach-renewal-windows\/\">timing outreach around the signal<\/a> instead of adding to a generic list. PredictLeads delivers all of it through API, flat files, webhooks, or MCP, is SOC 2 Type II certified, and sources only publicly available information under GDPR and CCPA, so the same signal stack works for a sales team\u2019s <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/blog\/use-data-enrichment-to-filter-and-prioritize-prospects\/\">data enrichment workflow<\/a> and a data team\u2019s compliance review.<\/p><h2><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span>Frequently Asked Questions<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n<h3><span class=\"ez-toc-section\" id=\"What_is_a_job_data_provider\"><\/span><span class=\"ez-toc-section\" id=\"What_is_a_job_data_provider\"><\/span>What is a job data provider?<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>A job data provider collects, structures, and delivers job posting records, typically sourced from employer websites, career pages, job boards, or applicant tracking systems, so businesses can analyze hiring activity without scraping it themselves. The structured version usually adds occupation classification, seniority, location, and sometimes salary data to the raw posting text. PredictLeads\u2019 <a href=\"https:\/\/predictleads.com\/job_listings\">Job Openings dataset<\/a> sources postings from company websites, career subpages, LinkedIn profiles, and ATS integrations, then classifies each one with O*NET occupation codes across 26 categories. That structuring step, not the raw posting itself, is what most GTM, data, and market intelligence teams are actually buying.<\/p>\n\n<h3><span class=\"ez-toc-section\" id=\"Which_job_data_provider_has_the_most_job_postings_in_2026\"><\/span><span class=\"ez-toc-section\" id=\"Which_job_data_provider_has_the_most_job_postings_in_2026\"><\/span>Which job data provider has the most job postings in 2026?<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Published job-posting totals vary widely by vendor and are not independently audited, so the answer depends on which public source you trust. PredictLeads publishes 300M+ Job Openings records since 2016 across 3M+ companies. Coresignal\u2019s own Datarade profile lists a larger figure, 468M+ job postings, while LinkUp\u2019s neutral profile lists roughly 200 million historical postings since 2007. Because these datasets measure different things (unique companies, raw postings, deduplicated postings), treat any single count as a starting point for evaluation, not a final scorecard entry.<\/p><h3><span class=\"ez-toc-section\" id=\"Is_PredictLeads_better_than_Coresignal_for_job_data\"><\/span><span class=\"ez-toc-section\" id=\"Is_PredictLeads_better_than_Coresignal_for_job_data\"><\/span>Is PredictLeads better than Coresignal for job data?<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>It depends on what you need the data to do. If you want job postings paired with technology detections, financing events, and a company firmographic layer in one API, PredictLeads is the only one of the two with that combination, backed by 300M+ Job Openings records and 1.96B+ Technology Detections. If you need a large, developer-first jobs, company, and employee dataset to structure yourself for a custom pipeline, Coresignal\u2019s public job postings dataset, listed at 468M+ records on its Datarade profile, is purpose-built for that. If you run GTM or RevOps, PredictLeads is often the better fit because hiring data on its own rarely explains why an account is worth contacting right now.<\/p><h3><span class=\"ez-toc-section\" id=\"Can_job_openings_data_be_combined_with_technographic_data_in_one_API\"><\/span><span class=\"ez-toc-section\" id=\"Can_job_openings_data_be_combined_with_technographic_data_in_one_API\"><\/span>Can job openings data be combined with technographic data in one API?<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Yes. PredictLeads\u2019 Technology Detections dataset provides evidence of which technologies a company uses or has recently used, built from 1.96B+ detections since 2018 across 98M+ company domains and five source types, including job descriptions that list a tool as a required skill. Pairing that with the same company\u2019s Job Openings record lets you check whether a hiring pattern lines up with a technology change instead of treating the two signals separately. None of the other four providers in this comparison publish an equivalent technographic layer alongside their job data.<\/p><h3><span class=\"ez-toc-section\" id=\"How_often_is_PredictLeads_job_openings_data_refreshed\"><\/span><span class=\"ez-toc-section\" id=\"How_often_is_PredictLeads_job_openings_data_refreshed\"><\/span>How often is PredictLeads job openings data refreshed?<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>PredictLeads refreshes job openings approximately every 36 hours, and high-traffic career pages are crawled multiple times daily. For teams that need to react the moment a new posting appears without waiting on a refresh cycle, PredictLeads also supports a JobOpeningsDataset webhook that pushes a notification the moment a new opening is found for a followed company. Webhooks and the <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/docs.predictleads.com\/mcp_integration\/introduction\">MCP server<\/a> are arranged for real-time and AI-agent workflows alongside the standard REST API.<\/p><h2><span class=\"ez-toc-section\" id=\"Ready_to_see_this_in_your_own_data\"><\/span><span class=\"ez-toc-section\" id=\"Ready_to_see_this_in_your_own_data\"><\/span>Ready to see this in your own data?<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Get 100 free API requests when you <a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/sign_up\">create an account<\/a>, no credit card, no sales call.<\/p><p><a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/sign_up\">Start Free \u2192<\/a><\/p><h2><span class=\"ez-toc-section\" id=\"Final_Thoughts_on_Job_Data_Providers\"><\/span><span class=\"ez-toc-section\" id=\"Final_Thoughts_on_Job_Data_Providers\"><\/span>Final Thoughts on Job Data Providers<span class=\"ez-toc-section-end\"><\/span><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If your hiring data workflow already includes a shortlist like this one, the next useful test is not a sales call, it is your own account list. Run a handful of real company domains through the <a href=\"https:\/\/predictleads.com\/job_listings\">PredictLeads Job Openings dataset<\/a> and compare whether pairing job openings with Technology Detections and Financing Events surfaces something a single-dataset provider\u2019s job feed would have missed on its own. The next post worth reading in this cluster covers monitoring a competitor\u2019s job postings directly for displacement and expansion signals.<\/p>\n<p><a target=\"_blank\" rel=\"dofollow\" class=\"text-blue-500 underline cursor-pointer\" href=\"https:\/\/predictleads.com\/sign_up\">Get 100 free API requests per month<\/a> and run the comparison yourself.<\/p><script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"BlogPosting\",\n  \"headline\": \"Top 5 Job Data Providers in October 2026: Coverage, Delivery, and Signals Compared\",\n  \"description\": \"Job data providers compared for October 2026: PredictLeads, Coresignal, Bright Data, LinkUp, and Revelio Labs scored on coverage, freshness, and delivery.\",\n  \"datePublished\": \"2026-04-29\",\n  \"dateModified\": \"2026-10-21\",\n  \"author\": {\n    \"@type\": \"Organization\",\n    \"name\": \"PredictLeads\",\n    \"url\": \"https:\/\/predictleads.com\"\n  },\n  \"publisher\": {\n    \"@type\": \"Organization\",\n    \"name\": \"PredictLeads\",\n    \"url\": \"https:\/\/predictleads.com\"\n  },\n  \"mainEntityOfPage\": {\n    \"@type\": \"WebPage\",\n    \"@id\": \"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/\"\n  },\n  \"url\": \"https:\/\/predictleads.com\/blog\/top-5-job-data-providers\/\",\n  \"wordCount\": 2550,\n  \"keywords\": [\n    \"job data providers\",\n    \"best job posting data providers\",\n    \"job data provider comparison\",\n    \"hiring data provider\",\n    \"job openings API\",\n    \"job openings dataset\",\n    \"technographic data\",\n    \"PredictLeads Job Openings\"\n  ]\n}\n<\/script>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is a job data provider?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"A job data provider collects, structures, and delivers job posting records, typically sourced from employer websites, career pages, job boards, or applicant tracking systems, so businesses can analyze hiring activity without scraping it themselves. The structured version usually adds occupation classification, seniority, location, and sometimes salary data to the raw posting text. PredictLeads' Job Openings dataset sources postings from company websites, career subpages, LinkedIn profiles, and ATS integrations, then classifies each one with O*NET occupation codes across 26 categories. That structuring step, not the raw posting itself, is what most GTM, data, and market intelligence teams are actually buying.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Which job data provider has the most job postings in 2026?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Published job-posting totals vary widely by vendor and are not independently audited, so the honest answer depends on which public source you trust. PredictLeads publishes 279.2M+ Job Openings records since 2018 across 2.9M+ websites. Coresignal's own Datarade profile lists a larger figure, 468M+ job postings, while LinkUp's neutral profile lists roughly 200 million historical postings since 2007. Because these datasets measure different things, treat any single count as a starting point for evaluation, not a final scorecard entry.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Is PredictLeads better than Coresignal for job data?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"It depends on what you need the data to do. If you want job postings paired with technology detections, financing events, and a company firmographic layer in one API, PredictLeads is the only one of the two with that combination, backed by 279.2M+ Job Openings records and 2B+ Technology Detections. If you need a large, developer-first jobs, company, and employee dataset to structure yourself, Coresignal's public job postings dataset, listed at 468M+ records on its Datarade profile, is purpose-built for that. Most GTM and RevOps teams choose PredictLeads because hiring data on its own rarely explains why an account is worth prioritizing right now.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can job openings data be combined with technographic data in one API?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. PredictLeads' Technology Detections dataset provides evidence of which technologies a company uses or has recently used, built from 2B+ detections since 2018 across 99M+ companies and five source types, including job descriptions that list a tool as a required skill. Pairing that with the same company's Job Openings record lets you check whether a hiring pattern lines up with a technology change instead of treating the two signals separately. None of the other providers in this comparison publish an equivalent technographic layer alongside their job data.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How often is PredictLeads job openings data refreshed?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"PredictLeads refreshes job openings approximately every 36 hours, and high-traffic career pages are crawled multiple times daily. For teams that need to react the moment a new posting appears, PredictLeads also supports a JobOpeningsDataset webhook that pushes a notification the moment a new opening is found for a followed company. Webhooks and the MCP server are arranged for real-time and AI-agent workflows alongside the standard REST API.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is the difference between a job board and a job data provider?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"A job board is a consumer-facing website where companies post openings and candidates apply, such as Indeed or LinkedIn Jobs. A job data provider instead collects those postings, often from employer websites directly rather than just boards, structures them into normalized fields, and delivers them as a dataset or API for analysis rather than for job seekers. PredictLeads sources Job Openings from company websites, career subpages, LinkedIn profiles, and ATS integrations, then classifies each one with O*NET occupation codes across 26 categories. That structuring step is what a GTM, data, or market intelligence team actually buys.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Does PredictLeads job openings data include salary information?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, when a posting discloses it. PredictLeads' Job Openings dataset carries both the raw salary field as written in the posting and a normalized salary_data field with low, high, currency, USD-converted value, and time unit, so salary ranges are comparable across postings that list pay differently. Not every posting discloses compensation, so coverage depends on how many employers in a given market disclose pay, which varies by region and job category. Where available, the raw text is preserved alongside the normalized figures.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is O*NET classification and why does it matter for hiring data?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"O*NET is the U.S. Department of Labor's standard occupational classification system, and it matters because it lets you compare job openings across companies and industries using a consistent taxonomy instead of matching on free-text job titles. PredictLeads classifies every job opening into O*NET occupation codes, occupation family, and occupation name, in addition to one of 26 broader job categories such as engineering, sales, or operations. Without this normalization, a provider's dataset forces you to write your own title-matching logic for every analysis. This is one of the six dimensions scored in this comparison.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I receive job openings data via webhooks instead of polling an API?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. PredictLeads supports a JobOpeningsDataset webhook that pushes a notification when a new job opening is found for a followed company, plus a separate JobOpeningsDeletedDataset webhook for closures, instead of requiring you to poll the API on a schedule. Webhooks are arranged through the PredictLeads sales team and respond with an HTTP 200 to confirm receipt. This delivery method suits real-time alerting and trigger-based workflows better than scheduled polling.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How is LinkUp's job data different from PredictLeads' job data?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"LinkUp focuses on postings sourced directly from employer career pages, with history back to 2007 and delivery mainly through data marketplaces such as Snowflake Data Marketplace and AWS Data Exchange, per its public data profile. PredictLeads covers a broader base, 2.9M+ websites with job openings and 710,600+ companies currently hiring, and pairs that data with Technology Detections, Financing Events, and company firmographics in the same API. LinkUp's neutral profile lists company coverage around 60,000, compared with PredictLeads' 120M+ company base. The right choice depends on whether you need a focused, market-research-style feed or a hiring signal pre-linked to other company context.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Is Bright Data a dedicated job data provider or a general web scraping tool?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Bright Data is primarily a web-data infrastructure company, known for proxy networks and a broad dataset marketplace, with a Jobs dataset and Jobs Scraper API as one category among many rather than its core focus. That makes it a flexible option for custom, on-demand collection from sites not covered by a packaged dataset, priced per record or per request. It does not publish an occupation-normalization layer, such as O*NET codes, as part of its jobs offering. Providers built specifically for hiring intelligence, like PredictLeads, typically ship that structuring already in place.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What industries use Revelio Labs' workforce analytics data?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Based on its public product description, Revelio Labs' customers include hedge funds and institutional investors using workforce data as an alternative-data signal for equity research, alongside HR teams and government organizations doing labor-market analysis. Its datasets focus on aggregated metrics such as attrition, compensation benchmarks, and headcount change rather than individual job postings meant for outbound prospecting. Revelio Labs has not published dataset scale figures on its neutral G2 or Datarade profiles. Teams that need a job opening tied to a specific company for sales or enrichment purposes typically look at a provider built for that connection instead.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I get PredictLeads job openings data as flat files for a data warehouse?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. PredictLeads delivers Job Openings and every other dataset as flat files, typically in JSONL format, through AWS S3, Google Cloud Storage, SFTP, or other enterprise arrangements, in addition to the REST API, webhooks, and MCP. Flat files are arranged through the sales team and suit bulk ingestion into a warehouse such as Snowflake or BigQuery, backfills, and large-scale analysis better than per-record API calls. The same underlying fields, including first_seen_at, last_seen_at, O*NET codes, and salary_data, carry over to the file delivery.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is behind-the-firewall technology detection and how does it relate to hiring signals?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Behind-the-firewall technology detection refers to evidence of enterprise tools, such as Salesforce or Snowflake, that do not always leave a visible trace on a company's public website, captured instead through signals like job descriptions that list the tool as a required skill. PredictLeads' Technology Detections dataset includes a behind_firewall boolean field for this reason, drawing on five detection sources: website script tags, DNS records, IP ranges, cookies, and job descriptions. A job posting that requires experience with a given platform is one of the sources used to detect that technology, not separate from hiring signals but a direct input to them. None of the other providers in this comparison publish an equivalent behind-the-firewall detection layer.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can AI agents query PredictLeads job openings data through MCP?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. PredictLeads runs a Model Context Protocol (MCP) server at mcp.predictleads.com that lets AI agents and tools like Cursor query Job Openings and every other PredictLeads dataset using natural language, such as asking whether a specific company is hiring for a given role. This is intended for conversational, agentic workflows rather than traditional API integration code, though it uses the same underlying API key and token credentials. MCP sits alongside API, flat files, and webhooks as the fourth PredictLeads delivery method.\"\n      }\n    }\n  ]\n}\n<\/script>\n\n","protected":false},"excerpt":{"rendered":"<p>Compare the top 5 job data providers by coverage, API delivery, freshness, and hiring signal depth for GTM and data teams (October 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center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[5],"tags":[36,11],"class_list":["post-877","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hiring-intent-data","tag-data","tag-hiring-intent"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.8 (Yoast SEO v28.8) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Top 5 Job Data Providers in October 2026<\/title>\n<meta name=\"description\" content=\"Compare the top 5 job data providers in September 2026 by API quality, dataset structure, and hiring signals to find the right fit for your\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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It tracks companies globally across hiring activity, technology usage, news events, financing, customer and partner relationships, similar companies, and other company-level signals. The data is delivered through APIs, flat files, webhooks, and MCP, allowing organizations to integrate company intelligence directly into their products, research workflows, AI agents, and internal systems. 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