Upwork Scraper: Extract Upwork Job Listings with Full Filter Support

Upwork Scraper pulls job listings from Upwork with the same filters you'd set manually: job type, experience level, budget range, duration, and workload. It handles Upwork's JavaScript-heavy pages automatically and returns clean, structured records with job details, required skills, and client data including total spend and hire history.

Use it to monitor new jobs in your niche, research what skills and rates the market is paying, build lead lists, or feed Upwork data into your own tools and dashboards.

Use cases

• Lead generation: spot clients posting jobs in your niche before competitors do
• Market research: track skill demand and budget trends across Upwork categories over time
• Hiring intelligence: monitor what roles are getting posted and at what rate or budget
• Competitive analysis: see how many proposals jobs attract and what experience levels clients want
• CRM enrichment: pull structured job and client data into spreadsheets or your own tooling
• Automated job alerts: run on a schedule and push new matches to Slack, email, or a webhook

Input

Parameter	Type	Default	Description
searchQuery	string	required	Keywords to search on Upwork (e.g. "python developer", "react frontend")
jobType	string	all	Payment type: all, hourly, or fixed
experienceLevel	array	[]	One or more of: entry, intermediate, expert. Empty means all.
sortBy	string	recency	Sort order: recency or relevance
hourlyRateMin	integer	—	Minimum hourly rate in USD
hourlyRateMax	integer	—	Maximum hourly rate in USD
budgetMin	integer	—	Minimum fixed project budget in USD
budgetMax	integer	—	Maximum fixed project budget in USD
duration	string	all	Project length: week, month, semester, ongoing, or all
workload	string	all	Weekly hours: part_time, full_time, or all
renderJs	boolean	true	Enable JavaScript rendering. Required for Upwork pages.
maxItems	integer	10	Number of job listings to return. Maximum is 10 (one page).
requestTimeoutSecs	integer	60	Per-request timeout in seconds
timeoutSecs	integer	300	Total actor run timeout in seconds

Example: scrape Python developer jobs (hourly, mid-to-senior level)

{
    "searchQuery": "python developer",
    "jobType": "hourly",
    "experienceLevel": ["intermediate", "expert"],
    "sortBy": "recency",
    "hourlyRateMin": 20,
    "hourlyRateMax": 80
}

Example: scrape fixed-price React jobs under $2,000

{
    "searchQuery": "react frontend developer",
    "jobType": "fixed",
    "experienceLevel": ["entry", "intermediate"],
    "budgetMin": 100,
    "budgetMax": 2000,
    "sortBy": "recency"
}

Example: monitor long-term data science contracts

{
    "searchQuery": "data scientist machine learning",
    "jobType": "hourly",
    "experienceLevel": ["expert"],
    "duration": "ongoing",
    "workload": "full_time",
    "sortBy": "relevance"
}

Example: entry-level writing jobs sorted by recency

{
    "searchQuery": "content writer blog articles",
    "jobType": "all",
    "experienceLevel": ["entry", "intermediate"],
    "sortBy": "recency"
}

Output

The actor stores results in a dataset. Each job listing produces one record:

{
    "jobId": "~01234567abcdef89",
    "jobTitle": "Senior Python Developer for Data Pipeline",
    "jobDescription": "Looking for an experienced Python developer to build an ETL pipeline that moves data from Salesforce into BigQuery on a daily schedule...",
    "skills": ["Python", "Apache Airflow", "PostgreSQL", "Google BigQuery", "AWS"],
    "category": "Web, Mobile & Software Dev",
    "subcategory": "Web Development",
    "jobPaymentType": "hourly",
    "experienceLevel": "Expert",
    "duration": "3 to 6 months",
    "workload": "30+ hrs/week",
    "hourlyRateMin": 30.0,
    "hourlyRateMax": 60.0,
    "fixedBudget": null,
    "budgetText": "$30.00-$60.00/hr",
    "clientCountry": "United States",
    "clientRating": 4.9,
    "clientJobsPosted": 47,
    "clientTotalSpent": 24500.0,
    "clientTotalHires": 12,
    "clientPaymentVerified": true,
    "proposalCount": "10 to 15",
    "connectsRequired": 6,
    "publishedAt": "2025-03-15T10:22:00.000+00:00",
    "postedAgo": "2 hours ago",
    "jobUrl": "https://www.upwork.com/jobs/~01234567abcdef89",
    "scrapedAt": "2025-03-20T08:45:12.123456+00:00"
}

Output fields reference

Field	Type	Description
jobId	string	Upwork job identifier
jobTitle	string	Job posting title
jobDescription	string	Job description text
skills	array	Required skill tags
category	string	Upwork job category
subcategory	string	Upwork job subcategory
jobPaymentType	string	hourly or fixed
experienceLevel	string	Entry, Intermediate, or Expert
duration	string	Expected project duration
workload	string	Weekly hours commitment (e.g. "30+ hrs/week")
hourlyRateMin	number	Min hourly rate in USD
hourlyRateMax	number	Max hourly rate in USD
fixedBudget	number	Fixed budget in USD
budgetText	string	Formatted budget or rate (e.g. "$30.00-$60.00/hr")
clientCountry	string	Client country
clientRating	number	Client feedback score (0-5)
clientJobsPosted	integer	Total jobs this client has posted
clientTotalSpent	number	Total USD spent by this client on Upwork
clientTotalHires	integer	Total freelancers hired by this client
clientPaymentVerified	boolean	Whether the client's payment method is verified
proposalCount	string	Proposal count range (e.g. "10 to 15")
connectsRequired	integer	Connects needed to apply
publishedAt	string	ISO 8601 timestamp the job was posted
postedAgo	string	Relative time since posting (e.g. "2 hours ago")
jobUrl	string	Direct URL to the job on Upwork
scrapedAt	string	ISO 8601 timestamp when the record was scraped

How it works

1. Builds a search URL from your query and active filters
2. Fetches the page using a rendering engine with residential proxy support
3. Parses job tiles directly from the rendered HTML
4. Transforms each job into a clean record and pushes it to the dataset
5. Returns up to 10 results from the first page of search results

What data does this actor extract?

For each job listing, the actor extracts the job title, full description, skill tags, category, subcategory, payment type, rate or budget, experience level, project duration, and workload. It also captures client-side data: country, feedback rating, payment verification status, total jobs posted, total amount spent on Upwork, total hires, proposal count, Connects required to apply, and a relative posted time (e.g. "2 hours ago").

FAQ

What if some fields come back null? Upwork's page structure changes over time. A null field means it was not present on that listing or the parser did not find it in the current layout. The record is still saved with all fields that were successfully extracted.

How many results can I get per run? Up to 10 per run — one page of Upwork search results. Use maxItems if you need fewer.

Can I filter by multiple experience levels at once? Yes. Pass an array to experienceLevel, for example ["intermediate", "expert"], and both filters are applied to the search.

Can I run this on a schedule? Yes. Use Apify's built-in scheduler with a cron expression. Pair it with a webhook to push new results to Slack, Google Sheets, or your CRM automatically after each run.

What happens if the page fails to load? The actor logs the HTTP status code and stops cleanly. Any results already collected are saved to the dataset.

Integrations

Connect Upwork Scraper with other apps and services using Apify integrations. You can integrate with Make, Zapier, Slack, Airbyte, GitHub, Google Sheets, Google Drive, and many more. You can also use webhooks to trigger actions whenever new job results are available.