{"id":884,"date":"2026-04-29T08:59:12","date_gmt":"2026-04-29T08:59:12","guid":{"rendered":"https:\/\/blog.predictleads.com\/?p=884"},"modified":"2026-10-21T15:01:36","modified_gmt":"2026-10-21T15:01:36","slug":"predictleads-job-openings-dataset-sales-growth","status":"publish","type":"post","link":"https:\/\/predictleads.com\/blog\/predictleads-job-openings-dataset-sales-growth\/","title":{"rendered":"PredictLeads Job Openings Dataset: Hiring Data for Sales Growth in October 2026"},"content":{"rendered":"\n<p><em>By Robert Fon \/ October 21, 2026<\/em><\/p>\n\n<p>Most sales teams still build target account lists from static firmographics: industry code, headcount band, and an estimated revenue range. None of those tell you which companies are expanding right now, which is the moment a prospect is most open to a new vendor conversation. The PredictLeads Job Openings dataset turns live job postings into a structured, queryable signal of company growth. This guide covers what the dataset contains, how its fields work, and how to turn hiring activity into a repeatable sales growth workflow.<\/p>\n\n<p><strong>TLDR:<\/strong><\/p>\n<ul>\n<li>The PredictLeads Job Openings dataset tracks postings from company career pages, ATS integrations, and LinkedIn profiles, then classifies every role with an O*NET occupation code.<\/li>\n<li>Hiring activity works as a growth signal because companies tend to add headcount when they expect demand and freeze it when they do not.<\/li>\n<li>Job Openings data is strongest when layered with Technology Detections, Financing Events, and News Events, since any single signal is noisy on its own.<\/li>\n<li>Every record carries first_seen_at, last_seen_at, and a status field, so you can time outreach to the moment a role appears rather than weeks later.<\/li>\n<li>PredictLeads has logged 279.2M+ job opening records since 2018 across 2.9M+ websites, with 10.2M active openings at any time and 710,600+ companies currently hiring.<\/li>\n<\/ul>\n\n<h2>What Is the PredictLeads Job Openings Dataset<\/h2>\n<p>The PredictLeads Job Openings dataset is a structured feed of job postings sourced from company career pages, career subpages, ATS integrations, and LinkedIn company profiles, with historical coverage back to 2018. Every posting is matched to a company record, deduplicated, and classified with an O*NET occupation code, so a &#8220;Senior Account Executive&#8221; posting from one company and a &#8220;Senior AE&#8221; posting from another land in the same queryable category. The dataset spans 26 job categories, from sales and engineering to operations and support, with seniority tracked from entry level through c_level.<\/p>\n\n<svg viewBox=\"0 0 1200 260\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" role=\"img\" aria-label=\"Job Openings dataset by the numbers, October 2026: 279.2 million plus job opening records since 2018, 10.2 million active openings at any time, 710,600 plus companies currently hiring, and 6.4 million plus new openings found last month.\" style=\"width:100%;height:auto;font-family:'Poppins','Segoe UI',system-ui,sans-serif;\">\n<title>Job Openings dataset by the numbers, October 2026<\/title>\n<rect x=\"0\" y=\"0\" width=\"1200\" height=\"260\" rx=\"24\" fill=\"#f3ecf8\"\/>\n<text x=\"600\" y=\"45\" text-anchor=\"middle\" font-size=\"26\" font-weight=\"800\" font-style=\"italic\" fill=\"#5b3d70\">Job Openings Dataset by the Numbers (October 2026)<\/text>\n<rect x=\"30\" y=\"70\" width=\"260\" height=\"160\" rx=\"16\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n<text x=\"160\" y=\"140\" text-anchor=\"middle\" font-size=\"34\" font-weight=\"800\" fill=\"#76508e\">279.2M+<\/text>\n<text x=\"160\" y=\"168\" text-anchor=\"middle\" font-size=\"14\" font-weight=\"600\" fill=\"#3a2f4d\"><tspan x=\"160\" dy=\"0\">job opening records<\/tspan><tspan x=\"160\" dy=\"18\">since 2018<\/tspan><\/text>\n<rect x=\"316\" y=\"70\" width=\"260\" height=\"160\" rx=\"16\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n<text x=\"446\" y=\"140\" text-anchor=\"middle\" font-size=\"34\" font-weight=\"800\" fill=\"#76508e\">10.2M<\/text>\n<text x=\"446\" y=\"168\" text-anchor=\"middle\" font-size=\"14\" font-weight=\"600\" fill=\"#3a2f4d\"><tspan x=\"446\" dy=\"0\">active openings<\/tspan><tspan x=\"446\" dy=\"18\">at any time<\/tspan><\/text>\n<rect x=\"602\" y=\"70\" width=\"260\" height=\"160\" rx=\"16\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n<text x=\"732\" y=\"140\" text-anchor=\"middle\" font-size=\"30\" font-weight=\"800\" fill=\"#76508e\">710,600+<\/text>\n<text x=\"732\" y=\"168\" text-anchor=\"middle\" font-size=\"14\" font-weight=\"600\" fill=\"#3a2f4d\"><tspan x=\"732\" dy=\"0\">companies currently<\/tspan><tspan x=\"732\" dy=\"18\">hiring<\/tspan><\/text>\n<rect x=\"888\" y=\"70\" width=\"260\" height=\"160\" rx=\"16\" fill=\"#ffffff\" stroke=\"#ece0f5\" stroke-width=\"2\"\/>\n<text x=\"1018\" y=\"140\" text-anchor=\"middle\" font-size=\"34\" font-weight=\"800\" fill=\"#76508e\">6.4M+<\/text>\n<text x=\"1018\" y=\"168\" text-anchor=\"middle\" font-size=\"14\" font-weight=\"600\" fill=\"#3a2f4d\"><tspan x=\"1018\" dy=\"0\">new openings found<\/tspan><tspan x=\"1018\" dy=\"18\">last month<\/tspan><\/text>\n<\/svg>\n\n<p>That scale, 279.2M+ historical records across 2.9M+ websites, is what makes hiring data usable as a repeatable sales signal rather than a handful of anecdotal postings.<\/p>\n\n<h2>What&#8217;s Inside Every Job Opening Record<\/h2>\n<p>Every record in the Job Openings dataset carries the same structured fields, whether the company is a two person startup or a public enterprise. That consistency is what makes the dataset queryable at scale instead of requiring you to parse free text postings yourself.<\/p>\n<ul>\n<li><strong>Role detail:<\/strong> title, normalized_title, translated_title, description, and categories (26 job categories).<\/li>\n<li><strong>Classification:<\/strong> onet_data (code, family, occupation_name) and a seniority field spanning levels from not_set and junior through director and c_level.<\/li>\n<li><strong>Timing:<\/strong> first_seen_at, last_seen_at, last_processed_at, posted_at, and a status field (closed or null).<\/li>\n<li><strong>Compensation and format:<\/strong> salary (raw) and salary_data (low, high, currency, USD value, time unit), plus contract_types (for example, full time, remote).<\/li>\n<li><strong>Location and contact:<\/strong> location, location_data, and recruiter_data (name, title, contact) where the posting includes it.<\/li>\n<\/ul>\n<p>Workplace type (remote, hybrid, onsite) is carried inside contract_types only when a posting states it, and many do not. In a PredictLeads analysis of 677,699 US technical job postings from July 18 to October 16, 2026, 46.3% carried no explicit workplace-type tag at all. Filtering only on that tag will miss a large share of otherwise matching roles, so pairing it with title and description keyword matching gives a more complete picture. Every role is also classified against the Department of Labor&#8217;s <a href=\"https:\/\/www.onetonline.org\/\" target=\"_blank\" rel=\"noopener\">O*NET occupational taxonomy<\/a>, the same standard used across US labor market research.<\/p>\n\n<h2>Job Openings Data vs. Firmographic Headcount Estimates<\/h2>\n<p>Firmographic headcount data answers one question: how big is this company. It is a useful baseline for segmentation, but it is usually refreshed on a slower cycle and gives you a range, not a timestamp of when the company actually started growing. PredictLeads Job Openings data answers a sharper question: where and what is this company staffing right now. The difference matters for timing an outreach motion, not just qualifying an account.<\/p>\n\n<table style=\"width:100%;border-collapse:collapse;margin:24px 0;font-size:15px;\">\n<thead>\n<tr style=\"background-color:#76508e;color:#ffffff;\">\n<th style=\"padding:12px 16px;text-align:left;border:1px solid #ece0f5;\">Dimension<\/th>\n<th style=\"padding:12px 16px;text-align:left;border:1px solid #ece0f5;\">Firmographic Headcount Data<\/th>\n<th style=\"padding:12px 16px;text-align:left;border:1px solid #ece0f5;\">PredictLeads Job Openings Data<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color:#ffffff;\">\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Update cadence<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Refreshed on a slower, periodic cycle<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Refreshed on an approximately 36 hour cadence<\/td>\n<\/tr>\n<tr style=\"background-color:#f3ecf8;\">\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Granularity<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Company wide headcount range<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Role level: title, O*NET code, seniority, category<\/td>\n<\/tr>\n<tr style=\"background-color:#ffffff;\">\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Timing signal<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">No timestamp on when growth started<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">first_seen_at and last_seen_at on every record<\/td>\n<\/tr>\n<tr style=\"background-color:#f3ecf8;\">\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">What it tells you<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">How big a company is<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Where and what a company is actively staffing right now<\/td>\n<\/tr>\n<tr style=\"background-color:#ffffff;\">\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Best use<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Firmographic segmentation and list building<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Timing outreach and prioritizing expanding accounts<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n<p>That timestamped, role level view is also why hiring activity is increasingly treated as a form of <a href=\"https:\/\/predictleads.com\/blog\/2026\/01\/07\/job-postings-as-alternative-data-why-hiring-activity-reveals-real-company-intent\/\">alternative data for company intent<\/a> rather than a simple headcount metric.<\/p>\n\n<h2>Why Hiring Is a Sales Growth Signal<\/h2>\n<p>Economists have treated job openings as a leading indicator of growth for decades. The U.S. Bureau of Labor Statistics publishes national job openings totals every month through its <a href=\"https:\/\/www.bls.gov\/jlt\/\" target=\"_blank\" rel=\"noopener\">Job Openings and Labor Turnover Survey (JOLTS)<\/a>, precisely because employers tend to add roles when they expect demand and pull back when they do not. Research groups such as <a href=\"https:\/\/www.hiringlab.org\/\" target=\"_blank\" rel=\"noopener\">Indeed Hiring Lab<\/a> track the same posting activity at a market level as a proxy for labor market momentum.<\/p>\n<p>The same logic holds at the company level, with more precision. A company posting a single replacement role is not a strong signal. A company posting five sales engineer roles in the same quarter, on the same team, is more likely allocating new budget toward go-to-market expansion. That is the distinction PredictLeads&#8217; seniority and onet_data fields let you make. For a deeper breakdown of separating backfill hiring from net-new growth, see our guide to <a href=\"https:\/\/predictleads.com\/blog\/2026\/04\/09\/job-openings-data-company-growth\/\">hiring signals as company growth indicators<\/a>.<\/p>\n\n<h2>Four Ways to Use Job Openings Data for Sales Growth<\/h2>\n<p>Turning hiring data into a sales growth motion is a four step process. The first two steps find and score the right accounts; the last two time and personalize the outreach. For a full walkthrough, see our <a href=\"https:\/\/predictleads.com\/blog\/2026\/04\/08\/how-to-use-job-openings-data-for-sales-prospecting\/\">guide to job openings data for sales prospecting<\/a>.<\/p>\n\n<h3>Steps 1 and 2: Find and Score the Right Accounts<\/h3>\n<ol>\n<li><strong>Define the trigger role.<\/strong> Use onet_codes on the discover\/job_openings endpoint to pull every company hiring for a role that signals your buying trigger, such as a first RevOps hire or a demand generation manager.<\/li>\n<li><strong>Score hiring velocity.<\/strong> Compare a company&#8217;s count of active openings against its 90 day found_at history to separate a one off backfill from a genuine team expansion.<\/li>\n<\/ol>\n\n<h3>Steps 3 and 4: Time and Personalize the Outreach<\/h3>\n<ol start=\"3\">\n<li><strong>Time the outreach to first_seen_at.<\/strong> Webhooks can push new JobOpeningsDataset records as they are found, so a rep can reach out within days of a role appearing instead of weeks later.<\/li>\n<li><strong>Personalize with the posting itself.<\/strong> Reference the specific skill, tool, or responsibility named in the job description, and use recruiter_data where available to route the message to the right contact.<\/li>\n<\/ol>\n\n<svg viewBox=\"0 0 1200 240\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" role=\"img\" aria-label=\"Four ways to use Job Openings data for sales growth: 1 define the trigger role using O*NET code and seniority, 2 score hiring velocity using active versus 90 day found_at, 3 time the outreach using first_seen_at and webhooks, 4 personalize the message using the description and recruiter_data.\" style=\"width:100%;height:auto;font-family:'Poppins','Segoe UI',system-ui,sans-serif;\">\n<title>Four ways to use Job Openings data for sales growth<\/title>\n<rect x=\"0\" y=\"0\" width=\"1200\" height=\"240\" fill=\"#ffffff\"\/>\n<rect x=\"20\" y=\"50\" width=\"250\" height=\"140\" rx=\"16\" fill=\"#efe4f6\" stroke=\"#9a78b0\" stroke-width=\"2\"\/>\n<text x=\"145\" y=\"105\" text-anchor=\"middle\" font-size=\"19\" font-weight=\"800\" fill=\"#5b3d70\">1. Define the Trigger<\/text>\n<text x=\"145\" y=\"135\" text-anchor=\"middle\" font-size=\"13\" font-weight=\"600\" fill=\"#3a2f4d\">O*NET code + seniority<\/text>\n<line x1=\"270\" y1=\"120\" x2=\"305\" y2=\"120\" stroke=\"#9a78b0\" stroke-width=\"3\"\/>\n<polygon points=\"305,113 318,120 305,127\" fill=\"#9a78b0\"\/>\n<rect x=\"310\" y=\"50\" width=\"250\" height=\"140\" rx=\"16\" fill=\"#efe4f6\" stroke=\"#9a78b0\" stroke-width=\"2\"\/>\n<text x=\"435\" y=\"105\" text-anchor=\"middle\" font-size=\"19\" font-weight=\"800\" fill=\"#5b3d70\">2. Score Velocity<\/text>\n<text x=\"435\" y=\"135\" text-anchor=\"middle\" font-size=\"13\" font-weight=\"600\" fill=\"#3a2f4d\">active vs. 90 day found_at<\/text>\n<line x1=\"560\" y1=\"120\" x2=\"595\" y2=\"120\" stroke=\"#9a78b0\" stroke-width=\"3\"\/>\n<polygon points=\"595,113 608,120 595,127\" fill=\"#9a78b0\"\/>\n<rect x=\"600\" y=\"50\" width=\"250\" height=\"140\" rx=\"16\" fill=\"#efe4f6\" stroke=\"#9a78b0\" stroke-width=\"2\"\/>\n<text x=\"725\" y=\"105\" text-anchor=\"middle\" font-size=\"19\" font-weight=\"800\" fill=\"#5b3d70\">3. Time Outreach<\/text>\n<text x=\"725\" y=\"135\" text-anchor=\"middle\" font-size=\"13\" font-weight=\"600\" fill=\"#3a2f4d\">first_seen_at + webhooks<\/text>\n<line x1=\"850\" y1=\"120\" x2=\"885\" y2=\"120\" stroke=\"#9a78b0\" stroke-width=\"3\"\/>\n<polygon points=\"885,113 898,120 885,127\" fill=\"#9a78b0\"\/>\n<rect x=\"890\" y=\"50\" width=\"250\" height=\"140\" rx=\"16\" fill=\"#efe4f6\" stroke=\"#9a78b0\" stroke-width=\"2\"\/>\n<text x=\"1015\" y=\"105\" text-anchor=\"middle\" font-size=\"19\" font-weight=\"800\" fill=\"#5b3d70\">4. Personalize<\/text>\n<text x=\"1015\" y=\"135\" text-anchor=\"middle\" font-size=\"13\" font-weight=\"600\" fill=\"#3a2f4d\">description + recruiter_data<\/text>\n<\/svg>\n\n<h2>Stack Job Openings With Other Signals for Higher Precision<\/h2>\n<p>A single hiring signal is directional. Three stacked signals are a pattern. Consider a company that posts three account executive openings at the mid_senior level, shows a recent technology detection tied to a CRM platform sourced from a job description, and logged an increases_headcount_by News Event in the same quarter. None of those three facts alone confirms a go to market expansion, but together they are stronger supporting evidence of one than any single record.<\/p>\n<p>This is also where PredictLeads&#8217; breadth matters more than any one dataset. See how hiring data pairs with technographics in <a href=\"https:\/\/predictleads.com\/blog\/2026\/04\/02\/job-openings-data-technographics\/\">Job Openings Data and Technographics<\/a>, and how the two combine into a single buying signal in <a href=\"https:\/\/predictleads.com\/blog\/2026\/05\/28\/job-postings-data-hiring-buying-signals\/\">Job Postings Data for GTM Buying Signals<\/a>. PredictLeads exposes Job Openings, Technology Detections, News Events, and Financing Events in one API account, so you query one source instead of reconciling exports from several vendors.<\/p>\n\n<h2>Why Job Openings Data Goes Stale, and How PredictLeads Keeps It Fresh<\/h2>\n<p>A target list built from job postings is only as useful as it is current. Roles close, get filled internally, or get reposted on a different board days later, and outreach aimed at a closed role reads as careless rather than well timed. PredictLeads addresses this with an approximately 36 hour refresh cadence and multiple crawls per day on high traffic career pages. Every record&#8217;s status field flips to closed (or stays null while open), and last_seen_at records the last confirmed sighting, so closed roles are marked rather than silently dropped. The pace of change is real: PredictLeads detected 6.4M+ new job openings in the most recent month and 57.2M+ in the trailing year.<\/p>\n\n<h2>How to Access the PredictLeads Job Openings Dataset<\/h2>\n<p>How you pull Job Openings data depends on whether you consume it through a tool or build it into your own pipeline. Data consumers querying one account at a time typically use the REST API. Data builders loading a full warehouse, or teams that need real time alerts, use flat files, webhooks, or the MCP server instead. All four methods return the same structured fields; they differ in latency and volume. Full parameters are documented at <a href=\"https:\/\/docs.predictleads.com\" target=\"_blank\" rel=\"noopener\">docs.predictleads.com<\/a>, including the <a href=\"https:\/\/predictleads.com\/blog\/2026\/05\/21\/job-openings-data-api-hiring-signals-products-crms\/\">job openings data API guide<\/a> for common query patterns.<\/p>\n\n<table style=\"width:100%;border-collapse:collapse;margin:24px 0;font-size:15px;\">\n<thead>\n<tr style=\"background-color:#76508e;color:#ffffff;\">\n<th style=\"padding:12px 16px;text-align:left;border:1px solid #ece0f5;\">Delivery Method<\/th>\n<th style=\"padding:12px 16px;text-align:left;border:1px solid #ece0f5;\">How It Works<\/th>\n<th style=\"padding:12px 16px;text-align:left;border:1px solid #ece0f5;\">Best For<\/th>\n<th style=\"padding:12px 16px;text-align:left;border:1px solid #ece0f5;\">Typical Use Case<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color:#ffffff;\">\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">REST API<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Query by company domain or discover endpoints with your API key and token<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Real time lookups and enrichment at request time<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Checking whether one target account is hiring before a call<\/td>\n<\/tr>\n<tr style=\"background-color:#f3ecf8;\">\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Flat Files<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Scheduled JSONL exports delivered to S3, Google Cloud Storage, or SFTP<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Bulk warehouse loads and historical backfills<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Loading the full history into Snowflake or BigQuery for modeling<\/td>\n<\/tr>\n<tr style=\"background-color:#ffffff;\">\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Webhooks<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Push notifications when a followed company&#8217;s JobOpeningsDataset changes<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Real time trigger workflows and alerting<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Notifying a rep the same day a target account posts a role<\/td>\n<\/tr>\n<tr style=\"background-color:#f3ecf8;\">\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">MCP Server<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Natural language queries through mcp.predictleads.com<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">AI agent workflows and conversational access<\/td>\n<td style=\"padding:12px 16px;border:1px solid #ece0f5;color:#3a2f4d;\">Asking an agent &#8220;is company X hiring SDRs&#8221; directly<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n<h2>How PredictLeads Delivers Job Openings Data at Scale<\/h2>\n<p>PredictLeads has logged 279.2M+ job opening records since 2018 across 2.9M+ websites, with 10.2M active openings at any time (9.6M carrying a full description) and 710,600+ companies currently hiring. That scale is maintained alongside PredictLeads&#8217; other datasets in the same API account: Technology Detections (2B+ detections since 2018, 240,000+ technologies, 99M+ companies with technology data), News Events (10M+ signals since 2016), Financing Events (210,800+ events since 2016), and Connections (371.8M+ relationships since 2019), among others.<\/p>\n<p>All of it is sourced from public information only, SOC 2 Type II certified, and handled under GDPR and CCPA. Explore the dataset on the <a href=\"https:\/\/predictleads.com\/job_listings\">Job Openings dataset page<\/a>, or pull it directly via API, flat files, webhooks, and MCP.<\/p>\n\n<h2>Final Thoughts on the Job Openings Dataset<\/h2>\n<p>Hiring data will not replace firmographic segmentation or intent signals, and it should not try to. What it adds is timing: a timestamped, role level view of where a company is actively investing, refreshed roughly every 36 hours. Used alongside Technology Detections, News Events, and Financing Events, it turns a static target list into a ranked one. For a wider comparison of vendors in this space, see our review of the <a href=\"https:\/\/predictleads.com\/blog\/2026\/04\/29\/top-5-job-data-providers-2026\/\">best job posting and hiring intent data providers for 2026<\/a>.<\/p>\n\n<h3>Ready to see this in your own data?<\/h3>\n<p>Get 100 free API requests per month &#8211; no credit card, no sales call.<\/p>\n<p><a href=\"https:\/\/predictleads.com\/sign_up?utm_source=blog&amp;utm_medium=cta&amp;utm_campaign=job_openings_dataset_refresh_2026_10\">Sign up for PredictLeads<\/a><\/p>\n\n<h2>Frequently Asked Questions<\/h2>\n\n<h3>What is the PredictLeads Job Openings dataset?<\/h3>\n<p>The PredictLeads Job Openings dataset is a structured, timestamped feed of job postings collected from company career pages, ATS integrations, and LinkedIn profiles, with history back to 2018. Every posting is normalized and classified with an O*NET occupation code, a seniority level, and one of 26 job categories, so you can query by role instead of parsing free text. As of October 2026, the dataset holds 279.2M+ historical records across 2.9M+ websites, with 710,600+ companies currently hiring. You can pull it by company through the API, in bulk through flat files, or as push notifications through webhooks.<\/p>\n\n<h3>How often is PredictLeads Job Openings data refreshed in 2026?<\/h3>\n<p>PredictLeads refreshes job openings on an approximately 36 hour cadence, and high traffic career pages are crawled multiple times a day. Every record carries first_seen_at and last_seen_at timestamps, so you can tell exactly when a role appeared and whether it is still listed. In the trailing 12 months, PredictLeads detected 57.2M+ job openings, including 6.4M+ in the most recent month alone, which shows the pace of change the refresh cadence is built to capture. Webhooks can also push new JobOpeningsDataset records to your system as soon as they are found, rather than waiting for a scheduled pull.<\/p>\n\n<h3>Can I filter PredictLeads job openings by O*NET code or seniority level?<\/h3>\n<p>Yes. Every job opening record includes onet_data (code, family, and occupation name) plus a seniority field spanning levels from not_set and junior through director and c_level, so you can target a specific function and rank in one query. The discover\/job_openings endpoint accepts onet_codes directly, alongside location and active_only filters, which lets you find every company hiring for a given role without looking up companies one at a time. This is the mechanism GTM teams use to build lists like &#8220;companies hiring a Head of Revenue Operations&#8221; without manual research. Postings are also grouped into 26 job categories, from sales and marketing to software development and operations, for broader filtering.<\/p>\n\n<h3>How is Job Openings data different from firmographic headcount estimates?<\/h3>\n<p>Firmographic headcount data gives you a company wide size range that is typically refreshed on a slower, periodic cycle, so it tells you how big a company is, not what it is doing right now. PredictLeads Job Openings data is role level and timestamped: it shows the specific title, O*NET code, and seniority of every open position, updated on an approximately 36 hour cadence. That granularity is what lets a sales team distinguish a company backfilling one role from one opening five new seats on the same team in the same month. PredictLeads tracks 10.2M active openings at any given time, which is the live layer that headcount estimates alone do not provide.<\/p>\n\n<h3>How do I access PredictLeads Job Openings data via API, flat files, webhooks, or MCP?<\/h3>\n<p>PredictLeads offers all four standard delivery methods for Job Openings data. The REST API (GET \/companies\/{domain}\/job_openings or GET \/discover\/job_openings) suits real time, per company lookups; flat files (typically JSONL, delivered to S3, Google Cloud Storage, or SFTP) suit bulk warehouse loads into Snowflake or BigQuery; webhooks push JobOpeningsDataset events as new postings are found; and the MCP server at mcp.predictleads.com lets AI agents query job openings in natural language. Full endpoint documentation, including filters like categories, seniority, and first_seen_at_from, is at docs.predictleads.com.<\/p>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"BlogPosting\",\n  \"headline\": \"PredictLeads Job Openings Dataset: Hiring Data for Sales Growth in October 2026\",\n  \"description\": \"Job Openings dataset from PredictLeads gives GTM teams hiring signals for sales growth: 279.2M+ records, O*NET codes, and webhooks. 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Every posting is normalized and classified with an O*NET occupation code, a seniority level, and one of 26 job categories, so you can query by role instead of parsing free text. As of October 2026, the dataset holds 279.2M+ historical records across 2.9M+ websites, with 710,600+ companies currently hiring. You can pull it by company through the API, in bulk through flat files, or as push notifications through webhooks.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How often is PredictLeads Job Openings data refreshed in 2026?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"PredictLeads refreshes job openings on an approximately 36 hour cadence, and high traffic career pages are crawled multiple times a day. Every record carries first_seen_at and last_seen_at timestamps, so you can tell exactly when a role appeared and whether it is still listed. 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The discover\/job_openings endpoint accepts onet_codes directly, alongside location and active_only filters, which lets you find every company hiring for a given role without looking up companies one at a time. This is the mechanism GTM teams use to build lists like companies hiring a Head of Revenue Operations without manual research. Postings are also grouped into 26 job categories, from sales and marketing to software development and operations, for broader filtering.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How is Job Openings data different from firmographic headcount estimates?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Firmographic headcount data gives you a company wide size range that is typically refreshed on a slower, periodic cycle, so it tells you how big a company is, not what it is doing right now. PredictLeads Job Openings data is role level and timestamped: it shows the specific title, O*NET code, and seniority of every open position, updated on an approximately 36 hour cadence. That granularity is what lets a sales team distinguish a company backfilling one role from one opening five new seats on the same team in the same month. PredictLeads tracks 10.2M active openings at any given time, which is the live layer that headcount estimates alone do not provide.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How do I access PredictLeads Job Openings data via API, flat files, webhooks, or MCP?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"PredictLeads offers all four standard delivery methods for Job Openings data. The REST API suits real time, per company lookups; flat files, typically JSONL delivered to S3, Google Cloud Storage, or SFTP, suit bulk warehouse loads into Snowflake or BigQuery; webhooks push JobOpeningsDataset events as new postings are found; and the MCP server at mcp.predictleads.com lets AI agents query job openings in natural language. Full endpoint documentation, including filters like categories, seniority, and first_seen_at_from, is at docs.predictleads.com.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What sources does PredictLeads use to find job openings?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"PredictLeads sources job openings from company websites and career subpages, ATS integrations, and LinkedIn company profiles, then deduplicates and normalizes them into one record format. Every record keeps a source URL and first_seen_at timestamp so you can trace exactly where and when a posting was found. Since 2018, this sourcing approach has produced 279.2M+ historical job opening records across 2.9M+ websites. Only publicly available posting pages are collected, consistent with PredictLeads' public sources only data policy.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Does PredictLeads include salary data in job postings?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Where a posting states compensation, PredictLeads captures it in two forms: a raw salary field as written, and a structured salary_data field with low, high, currency, USD normalized value, and time unit. Not every posting discloses pay, so salary fields are populated only when the source listing includes it rather than estimated. This lets you filter or sort active openings by disclosed compensation range when a workflow needs it. Salary is one of more than a dozen structured fields attached to every PredictLeads job opening, alongside title, seniority, and O*NET classification.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How many companies does PredictLeads track hiring activity for?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"PredictLeads has recorded job openings across 2.9M+ websites since 2018, and at any given time, 710,600+ companies are shown as currently hiring with at least one active, open posting. Active openings total 10.2M at any time, of which 9.6M carry a full description and are the subset PredictLeads exports to job boards. Coverage spans 195 countries, consistent with the scale of the broader PredictLeads Companies dataset. This breadth is what makes discover\/job_openings useful for finding every company hiring a given role, not just the companies you already track.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I combine Job Openings data with Technology Detections in one query?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. PredictLeads exposes Job Openings and Technology Detections in the same API account, so a query can combine companies hiring a DevOps engineer with companies showing evidence of using Kubernetes without reconciling two vendors. Technology Detections draws on 2B+ detections since 2018 covering 240,000+ technologies across 99M+ companies, including detections sourced from job descriptions that list a tool as a required skill. Stacking the two signals gives stronger supporting evidence of a stack transition or expansion than either dataset shows alone. Similar Companies, News Events, and Financing Events are available in the same API for further layering.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Is workplace type (remote, hybrid, onsite) a reliable filter on its own?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Treat it as a helpful filter, not a complete one. In a PredictLeads analysis of 677,699 US technical job postings from July 18 to October 16, 2026, 46.3% carried no explicit workplace type tag at all, so filtering only for remote or onsite will miss a large share of otherwise matching roles. When a posting does state it, the tag is carried inside the contract_types array, for example full time and remote together. Pairing the tag with title and description keyword matching gives a more complete picture than the tag alone.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What happens to a PredictLeads job opening record when the posting closes?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"PredictLeads does not delete a closed posting. Instead, the status field is set to closed, or left null while still open, and last_seen_at records the last time the posting was confirmed live, so you retain the full history rather than losing the record. This matters for sales teams that want to analyze past hiring waves, not just current openings, and for investors tracking hiring velocity over time. The active_only and not_closed filters on the API let you query just the live subset when that is all a workflow needs.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can investors use Job Openings data as a company growth signal?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Many investors and VC teams use hiring velocity, the rate of new job openings over time, as an early proxy for company growth, since headcount investment often precedes other visible signs of expansion. PredictLeads timestamps every record with first_seen_at, which supports exactly this kind of velocity and trendline analysis across a portfolio or a sourcing list. Pairing Job Openings data with PredictLeads Financing Events, 210,800+ events since 2016, can show whether hiring surges line up with a recent capital raise. Hiring data on its own indicates allocation, not a guaranteed outcome, so it works best alongside other PredictLeads datasets rather than in isolation.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Does PredictLeads send a webhook when a new job opening is found?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes, for accounts with webhooks configured through the PredictLeads sales team. The JobOpeningsDataset webhook fires when a new posting is found for a followed company, and a separate JobOpeningsDeletedDataset webhook fires when one is removed, with your system expected to respond HTTP 200 to confirm receipt. This is the delivery method built for real time trigger workflows, such as alerting a rep the same day a target account posts a relevant role. Webhooks complement, rather than replace, the REST API and flat file options.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is the difference between active and historical job openings in the PredictLeads dataset?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Active openings are the 10.2M postings PredictLeads shows as currently live at any given time; historical records are the full 279.2M+ archive back to 2018, including postings that have since closed. The active_only filter on the API and discover endpoints limits results to the live subset, while omitting it returns the full timestamped history for trend analysis. Historical depth is what lets you calculate a company's hiring velocity over months or years, not just its current headcount of open roles. Both active and historical records carry the same structured fields, including O*NET classification and seniority.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Is PredictLeads Job Openings data compliant with GDPR and CCPA?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. PredictLeads collects only publicly available information, such as job postings published on company career pages and LinkedIn, and the company maintains GDPR and CCPA compliance along with SOC 2 Type II certification. Job Openings records do not contain personal candidate data; recruiter_data, where present, reflects publicly listed hiring contact information on the posting itself, not personal job seeker information. PredictLeads is a company intelligence data provider, not a background check or people search service, which keeps its sourcing scope limited to employer published hiring activity. 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