apify-jobs-data

Jobs Data

Extract clean, structured job-posting data from 20+ boards in one Apify run, then put it to use. The pipeline is the same regardless of purpose:

acquire → de-noise → normalize → { analyze | export | rank by résumé }

The reusable value is the cleaned dataset: postings deduplicated across boards, cross-board reposts merged, likely ghost jobs flagged, and fields normalized to a consistent schema. Ghost-job and repost pollution corrupts demand counts, salary statistics, and dashboards just as much as it wastes a job seeker's time — so de-noise is the shared core, and every output mode runs on top of it.

Never invent a posting, a salary, a count, or a keyword. Everything is scraped live from a named Apify Actor and carries its source; missing fields stay blank, and every statistic reports the share of postings it is computed from — see Quality rules.

Ghost-job detection is heuristic and single-run: it compares the same (company, title, location) across boards for conflicting posted dates and reads the JD for pipeline language. The data carries no first-seen or edit history, so a flag is a suspicion, never a verdict. The Actors do the data work; the agent routes, de-noises, aggregates, and grounds:

Note on overlap with analysis-first job skills

A more analysis-first skill may also answer "hiring demand / in-demand skills / salary" questions. This skill's center of gravity is the cleaned dataset — cross-board duplicates and reposts merged, likely ghost jobs flagged, and fields normalized — which every mode then runs on. If you only need a quick market read, an analysis-only skill is lighter. Reach for this one when data quality matters (deduped demand counts, coverage-labeled salary stats), when you need the raw rows exported to CSV / an Apify dataset for a dashboard, or when you need a résumé ranked against live postings — none of which an analysis-only skill produces.