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First decide which job-listing data you are allowed to collect and use
“Publicly viewable” does not automatically mean available for bulk collection, indefinite retention, publication, or redistribution. The answer depends on the source, the access method, the data involved, and what you plan to do with the results. Define the scope before writing a crawler or extraction job.
Write down the collection and reuse scope
- Which countries or regions, occupations, employers, and sources are in scope?
- Which fields do you need, and how often will you retrieve them?
- Will results be used internally, published, or redistributed to customers?
- How long will you retain source content and derived records?
- What authorization, API access, or license covers each source and each use?
Check the current API documentation, terms, and license for every source, and confirm that access is approved for the intended use. Robots.txt can express crawl preferences, but it is not a grant of copyright, contract, or database rights. OpenAI’s crawler documentation illustrates that bot access settings may be distinct by bot and purpose; it does not state a policy for job boards: OpenAI crawler documentation.
Do not mistake posting integrations for job-search feeds
The cited LinkedIn and Indeed APIs are posting integrations, not general feeds for downloading all job listings. LinkedIn describes its Job Posting API as a way for members to post jobs from an applicant tracking system (ATS); access involves application and use-case review, approval, and continuing compliance with its terms. Those terms govern that API, not every job board: LinkedIn Job Posting API Terms and LinkedIn API overview.
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Indeed describes Job Sync as a GraphQL API for ATS partners to create, upsert, expire, and check the status of job postings. It is not documented there as an open search API for collecting the site’s listings: Indeed Job Sync API. An employer or ATS integration and a licensed research feed solve different problems; do not treat one as a substitute for the other.
Build an authorized acquisition pipeline
1. Prefer an approved API or licensed feed
Use the interface whose authorization covers your use. Record its source name, API or feed version where applicable, retrieval timestamp, and applicable limits with each collection run. Do not infer a permissible request rate from a throttle maximum. For example, LinkedIn’s cited overview labels API version 202604 as its latest version on that page and publishes an application maximum of 100,000 requests per UTC day. It also lists separate promoted-job throttles of 2,000 records per minute and 60,000 per day. These are limits for the documented integration, not a collection recommendation or a general job-search allowance; limits and versions can change. Check the current page before relying on them: LinkedIn API overview.
2. Keep source and field provenance
For each permitted record, retain the source identifier and the origin of important fields. A practical normalized record might contain:
- Identity: source, source listing ID, canonical URL, and retrieval time.
- Listing details: title, employer, location, employment type, posting date, and the original description or an authorized reference to it.
- Compensation and skills: original salary text, any cautiously normalized range, and extracted skills.
- Processing history: extraction schema or version, validation status, deduplication decision, and last refresh or expiry action.
This is a workflow suggestion, not a field list guaranteed by any cited API. Keep original salary wording when the currency, period, or range cannot be safely inferred. Represent absent or ambiguous values explicitly rather than filling them in.
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Normalize employer names, locations, job families, dates, and compensation for analysis, but preserve the source value alongside the normalized one. A remote role and an office-based role in the same city are not equivalent merely because a location field matches. Likewise, annual salary and hourly pay should not be compared as if they share a unit unless you have a documented conversion rule and the inputs support it.
Extract fields with AI, then validate them
AI is useful for turning varied prose into consistent candidate fields—such as skills, employment type, or a salary phrase—but a well-formed response is not proof that its contents are true. OpenAI Structured Outputs supports a subset of JSON Schema, including strings, numbers, booleans, integers, objects, arrays, enums, anyOf, and selected string formats. Schema-constrained output helps enforce shape; it does not establish that a skill or salary appears in the source: OpenAI Structured Outputs documentation.
Use explicit unknowns and evidence
Define fields with clear meanings and allowed values. Ask for null or an explicit unknown state when the source does not supply a value. For each inferred field, request a short evidence span or source snippet when practical. Then validate dates, numeric ranges, currencies, and enums deterministically. If an AI result says a listing offers $120,000–$150,000, check that exact range against the source text before treating it as a usable compensation value.
Example record contract
The following JSON illustrates a normalized record your pipeline could store. It is a suggested internal contract, not a schema promised by a job board or API.
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{
"source": "authorized_feed_name",
"source_listing_id": "listing-123",
"retrieved_at": "2026-09-29T12:00:00Z",
"title": "Data analyst",
"employer": "Example employer",
"location": "Toronto, ON",
"employment_type": null,
"salary_text": "CA$90,000–CA$110,000 a year",
"salary_min": 90000,
"salary_max": 110000,
"salary_currency": "CAD",
"skills": ["SQL", "Python"],
"posting_date": null,
"canonical_url": "https://example.com/jobs/123",
"evidence": {
"salary_text": "CA$90,000–CA$110,000 a year",
"skills": ["SQL", "Python"]
}
}Only populate normalized numbers when currency, units, and bounds are explicit enough to support them. Store source text or an authorized reference so reviewers can audit extraction and deduplication decisions.
Deduplicate records and manage freshness
Start with a stable source listing ID when the source supplies one. Where IDs are absent or change, compare normalized employer, title, location, and text similarity, and record the rule and outcome. Avoid merging distinct openings just because their titles and employers match; location, requisition number, team, or description may distinguish them.
Refresh and expire records according to the source’s terms and the purpose of the analysis. Indeed’s guidelines describe near-real-time update expectations for ATS integrations so that career-site data matches job data. That expectation applies to that integration context, not to research collection generally: Indeed API guidelines. Do not turn it into a universal freshness guarantee.
Turn normalized listings into defensible insights
Once records are deduplicated and timestamped, you can analyze skill frequency, explicitly stated salary ranges, remote or hybrid wording, location distribution, and change over time. For every published result, state the source universe and time window, the missing-field rate, the deduplication method, and relevant sampling limits. A set of listings describes posted demand in the sources you observed; it does not represent the entire labor market or prove actual hiring outcomes.
Compare feeds and integrations on the dimensions that matter
- Whether your organization is authorized for the intended collection and downstream use.
- Geographic and occupational coverage, and completeness of the fields you need.
- Refresh cadence, historical depth, and retention rights.
- Documented API limits and operational reliability.
- Duplicate rate, missing-field rate, and integration plus AI inference costs.
There is no supported basis here for ranking a particular feed or claiming a head-to-head extraction-accuracy result. Compare candidate sources against your needs and their actual authorization model rather than assuming they are interchangeable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use ScreenshotNeo only for the visual-evidence part of the workflow
ScreenshotNeo is a website screenshot API and MCP server, not a job-listing feed or a bulk search API. If you are authorized to access a particular listing page and need a visual capture to accompany an auditable record, a one-request screenshot can provide that image or PDF; it does not authorize collection or reuse of the page’s data.
For bulk structured job data, use an approved feed or API and the extraction workflow above. A screenshot captures rendered appearance, not a reliable substitute for source fields, stable IDs, or permission to retain listing content.
Or skip the browser setup
For an authorized page capture, one GET request returns an image. The call below uses the supplied example target URL; replace it with the listing URL you are permitted to capture. See the ScreenshotNeo documentation for request options.
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpScreenshotNeo accepts cookie or consent banners like a visitor and removes 60+ known consent platforms, newsletter popups, and chat widgets before capture; each step can be turned off. Bot checks or CAPTCHAs, blank pages, timeouts, failed loads, and cache hits cost nothing, and the response identifies the page verdict and billing status in headers. Its MCP server provides take_screenshot, get_page_info, and capture_pdf tools for AI agents. The free plan includes 1,000 screenshots per month with no card; paid plans start at $5 for 3,000. Those capabilities can help with visual documentation, but do not replace an authorized job-data source or validate AI-extracted facts.
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Troubleshoot common pipeline failures
Access denied, missing results, or unexpected throttling
First confirm that the credential, partner approval, use case, and requested operation match the source’s documented access. A posting API may accept employer or ATS operations without exposing search results. Check the documented version and limits, then use only the approved rate and scope; do not try to bypass access controls.
AI returns valid JSON with incorrect fields
Schema validity checks structure, not truth. Add evidence snippets, reject unsupported values, and run deterministic checks for ranges, dates, currencies, and allowed categories. Send low-confidence or contradictory records to review rather than silently accepting them.
Duplicate or stale listings distort trends
Inspect whether the same vacancy has multiple source IDs or URLs, and whether a deduplication rule merged separate locations or requisitions. Preserve the decision trail, recheck records on a schedule permitted by the source, and expire them when the applicable policy or analytical window requires it.
Salary and location comparisons do not add up
Keep original wording, currency, pay period, and location specificity. Do not convert a vague “competitive” salary into a number, equate hourly and annual pay without a defensible method, or treat a broad remote region as a city-level location. Report unknown and missing values instead of manufacturing precision.
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