# PeoplePerHour Job Scraper (`getdataforme/peopleperhour-job-scraper`) Actor Extract detailed job listings from PeoplePerHour.com with this Apify Actor. Scrape titles, descriptions, budgets, and client info from multiple queries. Features proxy support, rate limiting, and structured JSON output for market research, competitive intelligence, and trend analysis. - **URL**: https://apify.com/getdataforme/peopleperhour-job-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Jobs - **Stats:** 82 total users, 4 monthly users, 100.0% runs succeeded, 4 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $4.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## PeoplePerHour Job Scraper ### Introduction The PeoplePerHour Job Scraper is a powerful Apify Actor designed to extract job listings from PeoplePerHour.com, a popular freelance marketplace. It enables users to scrape detailed job data such as titles, descriptions, budgets, and client information based on custom search queries. This tool is ideal for freelancers, businesses, and researchers looking to gather market insights, monitor opportunities, or analyze trends in the freelance economy. ### Features - **Multi-Query Support**: Scrape jobs from multiple search queries simultaneously for comprehensive data collection. - **Detailed Job Data**: Extracts rich information including project IDs, URLs, budgets, categories, and client details. - **Proxy Integration**: Uses residential proxies to avoid IP blocks and ensure reliable scraping. - **Rate Limiting**: Built-in delays and best practices to respect website policies and prevent overloading. - **Structured Output**: Delivers clean JSON data, easily exportable to CSV, Excel, or databases. - **Customizable Limits**: Set maximum items to scrape, controlling data volume and processing time. - **Error Handling**: Robust mechanisms to handle failures, retries, and logging for smooth operations. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | queries | array | Yes | A list of search queries (e.g., job categories or keywords) to scrape job listings from PeoplePerHour. | ["Wordpress", "data engineering"] | | item_limit | integer | No | The maximum number of job items to scrape per query. Defaults to 30 if not specified. | 50 | | proxyConfiguration | object | No | Configuration for proxy servers to mask the scraper's origin. Defaults to using Apify's residential proxies. | {"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"]} | ### Example Usage #### Input Example ```json { "queries": ["data engineering", "Wordpress"], "item_limit": 30, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } } ```` #### Output Example ```json [ { "project_id": 4460080, "title": "Microsoft Power BI Fabric (Data Engineering) Consultant – Ad-Hoc", "url": "https://www.peopleperhour.com/freelance-jobs/technology-programming/data-science-analysis/microsoft-power-bi-fabric-data-engineering-consultant-4460080", "description": "I am seeking an experienced Microsoft Power BI Fabric (Data Engineering) Consultant for ad-hoc, on-and-off consulting assignments. The work will consist of short-term engagements focused on troubleshooting, optimisation, architecture review, and implementation tasks specifically within Microsoft Fabric.\n\nScope of Work\n\nMicrosoft Fabric Data Engineering solutions\nPerformance optimisation and issue resolution \nBest-practice implementation, solution structuring and troubleshooting\nGuidance on enterprise-grade Fabric architecture\nProfessional build-out or consulting based on requirements I will provide\n\nNote: I will supply all business logic, requirements, and content. Your role is to structure, implement, and optimise the solution professionally using Microsoft Fabric.\n\nRequired Expertise\n\nAdvanced, hands-on experience with Microsoft Fabric\nStrong background in Fabric Data Engineering and analytics workloads\nProven consulting experience delivering Fabric-based solutions\nAbility to work independently on short, focused assignments\n\n\u26a0\ufe0f Important:\nPlease do not apply if your experience is limited to general Power BI, Azure, or Synapse. This role requires deep, specialised Microsoft Fabric expertise at an advanced consulting level.\n\nPlease post you proposal to manoindi9 gmail com\n\nYour proposal or consulting approach are:-\n\nDetails of the services you offer\nWhat post-implementation or post-launch support you can provide\nRelevant experience with Microsoft Fabric projects", "budget": 50, "budget_converted": 67.42843951985226, "currency": "GBP", "budget_bracket": 3, "project_type": "hourly", "category": "Technology & Programming", "sub_category": "Data Science & Analysis", "posted_date": "2025-12-29 17:13:37", "expiry_date": "2026-01-28 17:13:37", "location_type": "remote", "proposal_count": 9, "client_name": "Mandy BI", "client_country": "United Kingdom", "query": "data engineering" } ] ``` ### Use Cases - **Market Research**: Analyze job trends, popular categories, and budget ranges in freelance markets. - **Competitive Intelligence**: Monitor competitors' job postings and client demands. - **Price Monitoring**: Track average rates for specific skills to inform pricing strategies. - **Content Aggregation**: Build datasets for reports on freelance opportunities. - **Academic Research**: Study labor market dynamics in the gig economy. - **Business Automation**: Automate lead generation for freelance services or recruitment agencies. ### Installation and Usage 1. Search for "PeoplePerHour Job Scraper" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters (e.g., queries and limits). 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The Actor outputs a JSON array of job objects. Each object includes fields like `project_id` (unique identifier), `title` (job title), `url` (link to the job), `description` (detailed job info), `budget` (original amount), `budget_converted` (USD equivalent), `currency`, `project_type` (e.g., hourly), `category` and `sub_category`, `posted_date` and `expiry_date`, `location_type`, `proposal_count`, `client_name`, `client_country`, and `query` (the search term used). This structured format ensures easy parsing and integration into workflows. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** # Actor input Schema ## `queries` (type: `array`): A list of URLs of the web pages you want to scrape data from. ## `item_limit` (type: `integer`): The maximum number of items to scrape. ## `proxyConfiguration` (type: `object`): Specifies proxy servers that will be used by the scraper in order to hide its origin. ## Actor input object example ```json { "queries": [ "Wordpress" ], "item_limit": 30, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "queries": [ "Wordpress" ], "item_limit": 30, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/peopleperhour-job-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "queries": ["Wordpress"], "item_limit": 30, "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("getdataforme/peopleperhour-job-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "queries": [ "Wordpress" ], "item_limit": 30, "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call getdataforme/peopleperhour-job-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/peopleperhour-job-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PeoplePerHour Job Scraper", "description": "Extract detailed job listings from PeoplePerHour.com with this Apify Actor. Scrape titles, descriptions, budgets, and client info from multiple queries. Features proxy support, rate limiting, and structured JSON output for market research, competitive intelligence, and trend analysis.", "version": "0.0", "x-build-id": "gpyAfEDRRlHl5CtJC" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~peopleperhour-job-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-peopleperhour-job-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~peopleperhour-job-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-peopleperhour-job-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~peopleperhour-job-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-peopleperhour-job-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "queries" ], "properties": { "queries": { "title": "Search Queries", "type": "array", "description": "A list of URLs of the web pages you want to scrape data from.", "items": { "type": "string" } }, "item_limit": { "title": "Max_limit", "type": "integer", "description": "The maximum number of items to scrape.", "default": 30 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Specifies proxy servers that will be used by the scraper in order to hide its origin.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```