LinkedIn Job Function Mapper

Map explicit job-function values from exports into your own versioned taxonomy.

Quick start

Paste this input into the Apify JSON input editor and start a run. The example uses synthetic records for a reproducible first audit.

{
  "provider": "harvestapi",
  "taxonomyVersion": "demo-v1",
  "crosswalk": [
    {
      "raw": "IT",
      "targets": [
        "information_technology"
      ]
    },
    {
      "raw": "PRJM",
      "targets": [
        "project_management"
      ]
    }
  ],
  "source": {
    "complete": true,
    "items": [
      {
        "id": "demo-1",
        "jobFunctions": [
          "IT",
          "PRJM"
        ]
      },
      {
        "id": "demo-2",
        "jobFunctions": [
          "IT",
          "UNMAPPED_CODE"
        ]
      },
      {
        "id": "demo-3",
        "title": "Software Engineer"
      }
    ]
  }
}

Results

Dataset rows include normalizedFunctions, unknownValues, evidence and taxonomyVersion; statuses include mapped, partially_mapped, unknown and conflicting_duplicate.

A platform run marked SUCCEEDED means execution finished. Always inspect the application status and unresolved rows before using the result.

Export sources

Use inline items with an explicit complete declaration, or a completed Apify runId. A datasetId alone does not prove collection is finished. Remote reads check run status and dataset stability. Maximum 10,000 records and 32 MiB per source. Local file paths are CLI-only.

The Actor processes data you supply; it does not collect new reviews, jobs or Maps listings. Remote datasets and previous key-value stores require access by the running account.

Limitations

You supply the crosswalk. Values are normalized for case, whitespace and Unicode; unknown values remain unresolved. No title inference, official LinkedIn taxonomy, or implicit splitting of compound labels.

Automation and data

Use Apify schedules or its API to run this Actor. Scheduling and external notifications are not enabled automatically. Results stay in the run dataset and key-value store; storage retention follows your Apify settings. Do not include passwords or session cookies in inputs.

Support

Report an issue with the Actor version, run ID, expected result and a small redacted input. Avoid including private customer data or credentials. Owlects is independent of the third-party platforms named here.

Pricing and run budget

Launch Actor fee: USD 0.03 per conclusive audit. Apify platform usage is charged separately. Unresolved or partial audits and blocked results do not generate an Actor fee. See the pricing panel for the active rates.

The maximum cost must cover all requested result fees before work starts. Use smaller batches to reduce the required budget. For remote Funda exports, the budget check uses the 10,000-record source limit.