Djinni.co Tech Jobs Scraper 💰 $0.95/1K — Salary, English Level

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What does Djinni.co Tech Jobs Scraper do?

Djinni.co Tech Jobs Scraper extracts structured job data from djinni.co — including salary data, apply URLs, company metadata, full descriptions, and remote-work indicators. It supports keyword search, location filters, and controllable result limits, so you can run the same query consistently over time. The actor also offers detail enrichment (full descriptions and company metadata) where the source provides them.

How to use this actor

• 👉 Register for a free Apify account — no credit card required.
• 🎉 Just click Sign up free on Apify → and complete a quick signup.
• 💰 A free Apify account includes $5 in monthly credits — enough to test this actor.
• ⏳ Scrape during the free trial, with no commitment or upfront payment required.

Key features

• ♻️ Incremental mode — recurring runs emit only NEW / UPDATED / REAPPEARED records — UNCHANGED and EXPIRED are opt-in. First run builds the baseline; subsequent runs emit and charge only for the diff. Pair with notifications for daily "new jobs" alerts to your hiring team. Saves 80–95% on daily monitoring.
• 🔔 Notifications — Telegram, Slack, Discord, WhatsApp Cloud API, generic webhook — out of the box. Pair with incremental + notifyOnlyChanges for daily "new Djinni jobs" pings to your hiring channel.
• 🔗 Paste-mode — paste any djinni.co URL straight from your browser — single-job pages, search-results URLs, or category SEO URLs. Build the search you want in the UI, copy the URL, paste it here.
• 🎯 Batch searches — batch ["python developer", "data engineer", "ML engineer"] in one run — one dedup state across all searches, single dataset, one Actor-Start charge instead of N.
• 📧 Email + phone extraction — every record carries extractedEmails[] and extractedPhones[] regex-pulled from the description — direct-outreach lists with no extra processing step.
• 🔗 URL + social-profile extraction — every record carries extractedUrls[] and structured socialProfiles { linkedin, twitter, github, … } parsed from the description — useful when employers drop their careers page or recruiter LinkedIn in-line.
• 📦 Compact mode — AI-agent and MCP-friendly compact payloads with core fields only — pipe straight into your ATS, salary-benchmarking tool, or LLM context without parsing extras.
• ✂️ Description truncation — cap description length with descriptionMaxLength to control LLM prompt cost and dataset size — set 0 for full descriptions, or any char-limit to trim.
• 📤 Export anywhere — Download the dataset as JSON, CSV, or Excel from the Apify Console, or stream live via the Apify API and integrations (Make, Zapier, Google Sheets, n8n, …).
• 💰 Structured salary — salary parsed into structured salaryMin / salaryMax / salaryCurrency / period — no string parsing on your side. Includes salaryHidden flag when the source filtered against a bracket but the listing itself doesn't disclose.
• 🔌 MCP connectors — export your results into Notion via Apify's MCP connectors — a clean run-summary page, no glue code. Opt-in via the App connector field; deterministic field-mapping, no AI. Built on Apify's connector framework, so more destinations open up as their catalog grows.

What data can you extract from djinni.co?

Each result includes Core listing fields (jobId, jobKey, title, location, salaryText, salaryMin, salaryMax, and salaryCurrency, and more), detail fields when enrichment is enabled (description, descriptionText, descriptionHtml, descriptionMarkdown, and descriptionLength), apply information (directApply), and company metadata (company, companyLogo, and companyUrl). In standard mode, all fields are always present — unavailable data points are returned as null, never omitted. In compact mode, only core fields are returned.

Enable detail enrichment in the input to get richer fields such as full descriptions and company metadata where the source provides them.

Input

The main inputs are a search keyword, an optional location filter, and a result limit. Additional filters and options are available in the input schema.

Key parameters:

• query — Keyword(s) to search for (e.g. "python", "java", "devops"). Use a JSON array ["python","go"] for multi-query. Leave empty to browse all tech jobs.
• location — City, country, or region. Use a JSON array for multi-location. Applied as a post-fetch filter against the listing's location text.
• startUrls — Direct djinni.co search or job detail URLs (e.g. https://djinni.co/jobs/keyword-python/).
• experienceLevel — Filter to listings requesting exactly N years of experience. Use a JSON array like ["1","3"] for multi-select. Applied post-fetch.
• englishLevel — Filter by required English level (A1, A2, B1, B2, C1, C2). Use a JSON array for multi-select. Applied post-fetch.
• isRemote — Restrict to full-remote listings (drops office and hybrid roles). Leave unchecked to return all. (default: false)
• salaryFrom — Drop listings whose published salary range is below this amount. 0 = no minimum. (default: 0)
• salaryTo — Drop listings whose published salary range exceeds this amount. 0 = no maximum. (default: 0)
• maxResults — Maximum total job listings to return (0 = unlimited). Primary cost control — Apify charges per emitted record. (default: 25)
• maxPages — Maximum SERP pages to scrape per source. Defensive bound — maxResults is the primary cap. (default: 5)
• includeKeywords — Require at least one of these keywords in title/description. Example: {"keywords":["react","node"],"matchTitle":true,"matchDescription":true}.
• excludeKeywords — Exclude jobs containing any of these keywords. Same config shape as includeKeywords.
• ...and 31 more parameters

Input examples

Basic search — Keyword-driven search with a result cap.

→ Full payload per result — all standard fields populated where the source provides them.

{
"query":"python",
"maxResults":50
}

Incremental tracking — Only emit jobs that changed since the previous run with this stateKey.

→ First run builds the baseline state. Subsequent runs emit only records that are new or whose tracked content changed. Set emitUnchanged: true to include unchanged records as well.

{
"query":"python",
"maxResults":200,
"incrementalMode":true,
"stateKey":"python-tracker"
}

Compact filtered output — Combine filters with compact mode for a lightweight AI-agent or MCP data source.

→ Core fields only — ideal for piping into LLMs or downstream tools without token overhead.

{
"query":"python",
"experienceLevel":"1",
"maxResults":50,
"compact":true
}

Output

Each run produces a dataset of structured job records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

Example job record

{
"jobId":"3c64f699b91922d9dbec45db9d902fd64fe3feaf5dbabe273e0fcdada7ea33a5",
"jobKey":"820617",
"title":"Senior Python Engineer (AI / Automation)",
"company":"Oxigen",
"companyLogo":"https://p.djinni.co/50/e25dae07f58da9ba8e802b0935edd6/Social_Media_On_Dark_400.png",
"companyUrl":"https://0xigen.com/",
"location":"Poland",
"description":"About the project\n \nUS-founded media company operating email newsletters at scale – millions of subscribers across finance, health, and business verticals. Small engineering team, flat structure, dire...",
"descriptionText":"About the project\n \nUS-founded media company operating email newsletters at scale – millions of subscribers across finance, health, and business verticals. Small engineering team, flat structure, dire...",
"descriptionHtml":"\n <p><strong>About the project</strong><br>&nbsp;</p><p>US-founded media company operating email newsletters at scale – millions of subscribers across finance, health, and business vertical...",
"descriptionMarkdown":"**About the project**\n\nUS-founded media company operating email newsletters at scale – millions of subscribers across finance, health, and business verticals. Small engineering team, flat structure, d...",
"descriptionLength":1840,
"salaryText":"$$$$",
"salaryCurrency":"USD",
"employmentType":"FULL_TIME",
"industry":"media",
"category":"Python",
"experienceMonths":72,
"experienceYears":6,
"englishLevel":"C1",
"isRemote":false,
"remoteText":"Office Work",
"directApply":true,
"postedAt":"2026-05-16T20:31:21.893242",
"postedAtRaw":"20:31 16.05.2026",
"validThrough":"2026-06-15T20:31:21.893242",
"canonicalUrl":"https://djinni.co/jobs/820617-senior-python-engineer-ai-automation/",
"sourceUrl":"https://djinni.co/jobs/820617-senior-python-engineer-ai-automation/",
"sourceDomain":"djinni.co",
"sourceCountry":"UA",
"searchQuery":"python",
"searchUrl":"https://djinni.co/jobs/keyword-python/",
"isSponsored":false,
"fetchedAt":"2026-05-16T20:03:24.649Z",
"detailFetched":true,
"contentQuality":"full",
"contentHash":"991bc78a6f011a7c5554070df3cd73823bcc3a510721a33505699c4dac254127",
"extractedUrls":[
"https://0xigen.com",
"https://djinni.nolt.io/trending",
"https://p.djinni.co/1c/67b58d21cc46ea54da35f8b4a2a12b/social-banner__3_.png",
"https://p.djinni.co/50/e25dae07f58da9ba8e802b0935edd6/Social_Media_On_Dark_400.png",
"https://px.ads.linkedin.com/collect/?pid=3623514&fmt=gif",
"https://snap.licdn.com/li.lms-analytics/insight.min.js"
],
"socialProfiles":{
"linkedin":"https://px.ads.linkedin.com/collect/?pid=3623514&fmt=gif"
}
}

Incremental fields

When incremental mode is on, each record also carries:

• changeType — one of NEW, UPDATED, UNCHANGED, REAPPEARED, EXPIRED. Default output covers NEW / UPDATED / REAPPEARED; set emitUnchanged: true or emitExpired: true to opt into the others.
• firstSeenAt, lastSeenAt — ISO-8601 timestamps tracking the listing across runs.
• isRepost, repostOfId, repostDetectedAt — populated when a new listing matches the tracked content of a previously expired one. Set skipReposts: true to drop detected reposts from the output.

How to scrape djinni.co

1. Go to Djinni.co Tech Jobs Scraper in Apify Console.
2. Enter a search keyword and optional location filter.
3. Set maxResults to control how many results you need.
4. Enable includeDetails if you need full descriptions, company data.
5. Click Start and wait for the run to finish.
6. Export the dataset as JSON, CSV, or Excel.

Use cases

• Extract job data from djinni.co for market research and competitive analysis.
• Track salary trends across regions and categories over time.
• Monitor new and changed listings on scheduled runs without processing the full dataset every time.
• Auto-apply or feed apply URLs into your ATS / hiring pipeline.
• Research company hiring patterns, employer profiles, and industry distribution.
• Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode.
• Export clean, structured data to dashboards, spreadsheets, or data warehouses.

How much does it cost to scrape djinni.co?

Djinni.co Tech Jobs Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.005 per run
• Per result: $0.00095 per job record

Example costs:

• 10 results: $0.014
• 25 results: $0.029
• 100 results: $0.1
• 200 results: $0.2
• 500 results: $0.48

Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of listings that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 100 results per run, daily polling (30 runs/month). Event-pricing examples scale linearly with result count.

Full re-scrape monthly cost at daily polling: $3.00. First month with incremental costs $0.38 / $0.66 / $1.07 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

Platform usage (compute and proxies) is billed separately by Apify based on actual consumption. Incremental runs consume less on result processing, though fixed per-run overhead stays the same.

FAQ

How many results can I get from djinni.co?

The number of results depends on the search query and available listings on djinni.co. Use the maxResults parameter to control how many results are returned per run.

Does Djinni.co Tech Jobs Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed listings on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate Djinni.co Tech Jobs Scraper with other apps?

Yes. Djinni.co Tech Jobs Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use Djinni.co Tech Jobs Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use Djinni.co Tech Jobs Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode, descriptionMaxLength, a single descriptionFormat, and excludeEmptyFields to keep payloads manageable for LLM context windows.

Is it legal to scrape djinni.co?

This actor extracts publicly available data from djinni.co. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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Getting started with Apify

New to Apify? Create a free account with $5 credit — no credit card required.

1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click Start — export results as JSON, CSV, or Excel

Need more later? See Apify pricing.