$1.2/1000
results
Search LinkedIn jobs by keyword with filters for location, date, experience, job type, remote/hybrid, and Easy Apply. Export titles, companies, locations, salary signals, and apply links. No cookie required. $1.20 per 1,000 jobs.
Search LinkedIn jobs by keyword with filters for location, date, experience, job type, remote/hybrid, and Easy Apply. Export titles, companies, locations, salary signals, and apply links. No cookie required. $1.20 per 1,000 jobs.
Programmatic job discovery for talent analytics, recruiting research, and HR tech pipelines.
On Scrapy.io, Data Doping LinkedIn Jobs Search by Data Doping is built for linkedin job api workflows — run from the Store, schedule recurring jobs, or POST to datadoping.p.scrapy.io from your stack.
max_jobs for predictable spendLinkedIn is the system of record for B2B identity — profiles, company updates, and post feeds that power ABM and competitive intelligence. A linkedin job api pipeline on Scrapy.io converts public URLs into structured JSON without maintaining cookie-based browser farms.
Data Doping LinkedIn Jobs Search is built for API-first batch jobs with caps you control. Schedule runs, call datadoping.p.scrapy.io, or chain Data Doping LinkedIn tools on the same Scrapy.io billing account.
This linkedin job api on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover Data Doping LinkedIn Jobs Search while researching linkedin job api and related terms such as linkedin jobs api, linkedin api jobs, scrape jobs, jobs scraper. This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of Data Doping LinkedIn Jobs Search:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetdatadoping.p.scrapy.io/linkedin-job-search-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Data Doping LinkedIn Jobs Search so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Search public LinkedIn job listings by keyword with optional filters.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"keywords": "Software Engineer",
"geoIds": [
"103644278"
],
"jobTypes": [
"F",
"P",
"C",
"T",
"V"
],
"location": "United States",
"max_jobs": 100,
"workTypes": [
"onsite",
"remote",
"hybrid"
]
}| Field | Type | Required | Description |
|---|---|---|---|
keywords | string | Yes | Required. Job title, skills, or any search text (e.g. role name, stack, company type) |
geoIds | string[] | Yes | Optional. One or more LinkedIn geoId values as strings. Examples: United States — 103644278; United Kingdom — 101165590; India — 102713980; Canada — 101174742; Germany — 101282230; France — 105015875 |
jobTypes | string | Yes | Optional. LinkedIn employment type codes. Choose any combination; remove what you do not want |
location | string | No | Optional. Free-text place: country, city, region, or metro (e.g. United States, London, New York) |
max_jobs | integer | No | How many job rows to store in the dataset (minimum 10). Examples: 10, 50, 100, 500 |
workTypes | string | Yes | Optional. Choose one or more: on-site, remote, hybrid. Remove any you do not want to include |
Results appear in the Scrapy.io run Output tab. Jobs and Errors tables in the run output.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"title": "Senior Data Engineer",
"company": "Example Co",
"location": "Remote",
"job_url": "https://www.linkedin.com/jobs/view/1234567890",
"posted_at": "2026-08-20T00:00:00.000Z"
}Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
track hiring trends by role and geography
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Data Doping with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
monitor competitor job postings
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Data Doping with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
surface companies actively hiring in a niche
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Data Doping with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
estimate open-role volume by keyword
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Data Doping with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
Data Doping delivers lightweight LinkedIn enrichment on Scrapy.io — company cards, job search, and post parsing designed to chain with your CRM and warehouse pipelines.
Data Doping LinkedIn Jobs Search is rarely the only step in a data workflow. Teams typically:
See Related Data Doping tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersData Doping LinkedIn Jobs Search separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Data Doping tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Maps, YouTube, LinkedIn, Instagram, or TikTok extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. Data Doping LinkedIn Jobs Search on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Data Doping listings add warehouse-ready documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
Data Doping LinkedIn Jobs Search runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run Data Doping LinkedIn Jobs Search once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
datadoping.p.scrapy.io on a queueData Doping tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://datadoping.p.scrapy.io/linkedin-job-search-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"keyword": "data engineer",
"location": "United States",
"max_jobs": 50
}'Bulk runs via the scraper endpoint:
curl -X POST "https://datadoping.p.scrapy.io/linkedin-job-search-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"keyword": "data engineer",
"location": "United States",
"max_jobs": 50
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Data Doping LinkedIn Jobs Search is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on Data Doping LinkedIn Jobs Search for MCP connection snippets.
| Tool | Best for |
|---|---|
| Data Doping Instagram Profile Batch Lookup | Data Doping companion tool — overview |
| Data Doping LinkedIn Company Profiles | Data Doping companion tool — overview |
| Data Doping LinkedIn Post Parser | Data Doping companion tool — overview |
Batch keywords in API jobs with per-row error isolation
Store job_url as the stable warehouse key
Start with low max_jobs until Output shape is validated
Inspect per-row errors in the Errors output view instead of failing an entire batch
Enrichment jobs misbehaving? Raise Issues on this Data Doping listing with run ID and the company slug, job keyword, or profile URL you used
Data Doping LinkedIn Jobs Search collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$1.20 per 1,000 jobs — billed per successful row on Scrapy.io. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://datadoping.p.scrapy.io/linkedin-job-search-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query Data Doping LinkedIn Jobs Search from AI assistants and agent workflows.
Data Doping keeps Instagram and LinkedIn enrichment lightweight for pipeline teams — leave a Reviews note if this scrape chains well with your CRM and which lookup you run most.
Programmatic job discovery for talent analytics, recruiting research, and HR tech pipelines.
On Scrapy.io, Data Doping LinkedIn Jobs Search by Data Doping is built for linkedin job api workflows — run from the Store, schedule recurring jobs, or POST to datadoping.p.scrapy.io from your stack.
max_jobs for predictable spendLinkedIn is the system of record for B2B identity — profiles, company updates, and post feeds that power ABM and competitive intelligence. A linkedin job api pipeline on Scrapy.io converts public URLs into structured JSON without maintaining cookie-based browser farms.
Data Doping LinkedIn Jobs Search is built for API-first batch jobs with caps you control. Schedule runs, call datadoping.p.scrapy.io, or chain Data Doping LinkedIn tools on the same Scrapy.io billing account.
This linkedin job api on Scrapy.io exports structured fields including:
For your first run, keep caps low (maxResults, maxReviews, etc.) until you validate row shape in the Output preview.
Teams discover Data Doping LinkedIn Jobs Search while researching linkedin job api and related terms such as linkedin jobs api, linkedin api jobs, scrape jobs, jobs scraper. This listing is structured to answer common questions — including — with concrete Input and Output examples you can run on Scrapy.io without maintaining your own scraper infrastructure.
If you arrived from SEO or paid search, start with the Input JSON example below, run once on the free tier, then scale with schedules or the publisher API once row shape and cost look right.
Use this checklist before your first production run of Data Doping LinkedIn Jobs Search:
maxResults, maxReviews, or equivalent limits so a full batch stays within budgetdatadoping.p.scrapy.io/linkedin-job-search-scraper/v1/api or create a scheduled jobMost teams keep a "golden" input file in git for Data Doping LinkedIn Jobs Search so staging and production use identical payloads. Update that file when you add keywords, targets, or communities — not the orchestration code around it.
Search public LinkedIn job listings by keyword with optional filters.
Configure the run in the Scrapy.io input form or pass JSON to the publisher API.
{
"keywords": "Software Engineer",
"geoIds": [
"103644278"
],
"jobTypes": [
"F",
"P",
"C",
"T",
"V"
],
"location": "United States",
"max_jobs": 100,
"workTypes": [
"onsite",
"remote",
"hybrid"
]
}| Field | Type | Required | Description |
|---|---|---|---|
keywords | string | Yes | Required. Job title, skills, or any search text (e.g. role name, stack, company type) |
geoIds | string[] | Yes | Optional. One or more LinkedIn geoId values as strings. Examples: United States — 103644278; United Kingdom — 101165590; India — 102713980; Canada — 101174742; Germany — 101282230; France — 105015875 |
jobTypes | string | Yes | Optional. LinkedIn employment type codes. Choose any combination; remove what you do not want |
location | string | No | Optional. Free-text place: country, city, region, or metro (e.g. United States, London, New York) |
max_jobs | integer | No | How many job rows to store in the dataset (minimum 10). Examples: 10, 50, 100, 500 |
workTypes | string | Yes | Optional. Choose one or more: on-site, remote, hybrid. Remove any you do not want to include |
Results appear in the Scrapy.io run Output tab. Jobs and Errors tables in the run output.
Each successful row is designed for warehouse loads: stable column names, explicit error rows, and caps you control in the input form.
{
"title": "Senior Data Engineer",
"company": "Example Co",
"location": "Remote",
"job_url": "https://www.linkedin.com/jobs/view/1234567890",
"posted_at": "2026-08-20T00:00:00.000Z"
}Download the full dataset as JSON, CSV, or Excel from the Output tab, or fetch results programmatically via the publisher API after the run completes.
track hiring trends by role and geography
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Data Doping with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
monitor competitor job postings
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Data Doping with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
surface companies actively hiring in a niche
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Data Doping with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
estimate open-role volume by keyword
Teams usually snapshot these rows on a schedule, store a scrapedAt timestamp in their warehouse, and diff week-over-week to spot new entries or metric changes. For first-time setup, run Data Doping with conservative caps, validate the Output preview against your schema, then promote the same JSON to a cron or API job. Document which Output view (Posts, Comments, Reviews, etc.) your downstream models consume so new teammates can reproduce the pipeline without tribal knowledge.
Data Doping delivers lightweight LinkedIn enrichment on Scrapy.io — company cards, job search, and post parsing designed to chain with your CRM and warehouse pipelines.
Data Doping LinkedIn Jobs Search is rarely the only step in a data workflow. Teams typically:
See Related Data Doping tools below for same-publisher companions you can chain without leaving the Scrapy.io billing account.
Most production deployments follow the same shape:
postUrl, placeId, videoId, reviewId, …) plus scrape date where history mattersData Doping LinkedIn Jobs Search separates successful rows from per-input errors, so staging logic can WHERE type != 'error' (or the equivalent filter for your tool) without failing the whole batch. Join across Data Doping tools on those stable keys to build multi-step datasets without re-scraping upstream steps.
Maintaining Maps, YouTube, LinkedIn, Instagram, or TikTok extractors in-house means handling rate limits, HTML or API changes, proxy rotation, and observability — work that rarely differentiates your product. Data Doping LinkedIn Jobs Search on Scrapy.io outsources that operational burden: you supply inputs, Scrapy.io runs the job, and you pay per successful row.
Data Doping listings add warehouse-ready documentation (clear caps, field lists, and pipeline patterns) so data teams can onboard without reading executor source code. When requirements change, update the input JSON or schedule — not your scraper fleet.
Data Doping LinkedIn Jobs Search runs on Scrapy.io support multiple ways to get data out:
For production pipelines, most teams land JSON in object storage, normalize in dbt or Spark, and schedule recurring runs so the Store listing becomes just another data source — not a manual step.
Run Data Doping LinkedIn Jobs Search once from the Store to validate inputs, then promote the same JSON payload to a scheduled job on Scrapy.io. Common patterns:
datadoping.p.scrapy.io on a queueData Doping tools use pay-per-result billing, so scheduled jobs stay predictable when you cap maxResults, max_posts, max_comments, or equivalent limits in the input form.
curl -X POST "https://datadoping.p.scrapy.io/linkedin-job-search-scraper/v1/api" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"keyword": "data engineer",
"location": "United States",
"max_jobs": 50
}'Bulk runs via the scraper endpoint:
curl -X POST "https://datadoping.p.scrapy.io/linkedin-job-search-scraper/v1/scraper" \
-H "Content-Type: application/json" \
-H "X-API-Key: YOUR_API_KEY" \
-d '{
"keyword": "data engineer",
"location": "United States",
"max_jobs": 50
}'Open the API tab on the Store listing for Python, JavaScript, cURL, OpenAPI, and MCP snippets.
Data Doping LinkedIn Jobs Search is available through the Scrapy.io MCP server — configure your API key in Cursor, Claude Desktop, or another MCP client, then invoke the tool from agent workflows. This is useful for ad-hoc research ("scrape these ten URLs and summarize the Output") without writing boilerplate HTTP code.
For production, prefer the publisher API or scheduled runs; use MCP for exploratory analysis and internal tooling where a human or agent supplies inputs interactively. See the API tab on Data Doping LinkedIn Jobs Search for MCP connection snippets.
| Tool | Best for |
|---|---|
| Data Doping Instagram Profile Batch Lookup | Data Doping companion tool — overview |
| Data Doping LinkedIn Company Profiles | Data Doping companion tool — overview |
| Data Doping LinkedIn Post Parser | Data Doping companion tool — overview |
Batch keywords in API jobs with per-row error isolation
Store job_url as the stable warehouse key
Start with low max_jobs until Output shape is validated
Inspect per-row errors in the Errors output view instead of failing an entire batch
Enrichment jobs misbehaving? Raise Issues on this Data Doping listing with run ID and the company slug, job keyword, or profile URL you used
Data Doping LinkedIn Jobs Search collects publicly available data only. You are responsible for complying with platform terms, applicable laws, and data-protection regulations (including GDPR when personal data is involved). Consult your legal team for your specific use case.
$1.20 per 1,000 jobs — billed per successful row on Scrapy.io. You are charged only for successfully scraped rows. See Scrapy.io pricing for plan details.
Yes. Use webhooks, scheduled runs, or the publisher API to push data into your warehouse, CRM, or automation stack.
Yes. POST to https://datadoping.p.scrapy.io/linkedin-job-search-scraper/v1/api with your API key. See the API tab on the Store listing for ready-made snippets.
Yes. Configure the Scrapy.io MCP endpoint with your API key to query Data Doping LinkedIn Jobs Search from AI assistants and agent workflows.
Data Doping keeps Instagram and LinkedIn enrichment lightweight for pipeline teams — leave a Reviews note if this scrape chains well with your CRM and which lookup you run most.