# Zoominfo Job Post Extractor (`getdataforme/zoominfo-job-post-extractor`) Actor The Zoominfo Job Post Extractor efficiently scrapes job postings, providing insights into hiring trends and technology stacks. It captures comprehensive data like company details, salary, and tech stack with high accuracy.... - **URL**: https://apify.com/getdataforme/zoominfo-job-post-extractor.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** AI, Automation, E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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 --- ## Zoominfo Job Post Extractor ### Introduction The **Zoominfo Job Post Extractor** is a powerful tool designed to efficiently scrape job postings from Zoominfo. It provides detailed insights into company hiring trends, industry demands, and technology stacks, making it an invaluable resource for market research, competitive intelligence, and business strategy development. ### Features - **Comprehensive Data Extraction**: Captures essential details such as company name, industry, department, job title, location, salary, and more. - **High-Quality Data**: Ensures reliable and accurate data extraction with minimal errors. - **Customizable Limits**: Allows users to set a maximum number of items to scrape per run for tailored data collection. - **Advanced Technical Configurations**: Offers performance settings to optimize scraping efficiency. - **User-Friendly Interface**: Easy setup and execution within the Apify platform. ### Input Parameters Table | Parameter | Type | Required | Description | Example | |-------------|---------|----------|-----------------------------------------------------------------------------|---------| | item_limit | integer | No | Maximum items to scrape per actor run. Set to 0 for no limit. | `10` | ### Example Usage #### Input JSON ```json { "item_limit": 5 } ```` #### Output JSON ```json [ { "company_name": "OpenAI", "industry": "Artificial Intelligence", "dept": "Research", "title": "Research Scientist, Alignment", "status": "Open", "location": "San Francisco, CA", "seniority": "Senior", "salary": "$295K–$440K", "dept_label": "Research", "meta_items_raw": ["San Francisco, CA", "Senior", "$295K–$440K", "Research"], "tech_stack": ["Python", "PyTorch", "RLHF", "JAX", "Transformers"], "tech_stack_visible": ["Python", "PyTorch", "RLHF", "JAX"], "tech_stack_more": "+1 more", "raw_text": "Research Scientist, Alignment Open View San Francisco, CA Senior $295K–$440K Research Python PyTorch RLHF JAX +1 more", "actor_id": "BSsviGiYbb5hnWjD5", "run_id": "VFRGWjmehW2XaoD2K" }, { "company_name": "OpenAI", "industry": "Artificial Intelligence", "dept": "Engineering", "title": "Staff SWE, API Platform", "status": "Open", "location": "San Francisco, CA", "seniority": "Staff", "salary": "$275K–$385K", "dept_label": "Engineering", "meta_items_raw": ["San Francisco, CA", "Staff", "$275K–$385K", "Engineering"], "tech_stack": ["Python", "Go", "Kubernetes", "PostgreSQL", "Redis"], "tech_stack_visible": ["Python", "Go", "Kubernetes", "PostgreSQL"], "tech_stack_more": "+1 more", "raw_text": "Staff SWE, API Platform Open View San Francisco, CA Staff $275K–$385K Engineering Python Go Kubernetes PostgreSQL +1 more", "actor_id": "BSsviGiYbb5hnWjD5", "run_id": "VFRGWjmehW2XaoD2K" }, { "company_name": "OpenAI", "industry": "Artificial Intelligence", "dept": "Engineering", "title": "ML Engineer, Post-Training", "status": "Open", "location": "San Francisco, CA", "seniority": "Senior", "salary": "$280K–$400K", "dept_label": "Engineering", "meta_items_raw": ["San Francisco, CA", "Senior", "$280K–$400K", "Engineering"], "tech_stack": ["Python", "PyTorch", "CUDA", "Triton", "RLHF"], "tech_stack_visible": ["Python", "PyTorch", "CUDA", "Triton"], "tech_stack_more": "+1 more", "raw_text": "ML Engineer, Post-Training Open View San Francisco, CA Senior $280K–$400K Engineering Python PyTorch CUDA Triton +1 more", "actor_id": "BSsviGiYbb5hnWjD5", "run_id": "VFRGWjmehW2XaoD2K" } ] ``` ### Use Cases - **Market Research and Analysis**: Gain insights into hiring trends and industry demands. - **Competitive Intelligence**: Monitor competitor job postings to understand strategic directions. - **Price Monitoring**: Analyze salary ranges across different roles and industries. - **Content Aggregation**: Collect data for newsletters or reports on job market trends. - **Academic Research**: Use job posting data for studies in labor economics or business strategy. - **Business Automation**: Integrate with HR systems to streamline recruitment processes. ### Installation and Usage 1. Search for "Zoominfo Job Post Extractor" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 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 output is a JSON array of job postings, each containing: - `company_name`: Name of the company - `industry`: Industry sector - `dept`: Department - `title`: Job title - `status`: Current status of the posting - `location`: Location of the job - `seniority`: Seniority level - `salary`: Salary range - `meta_items_raw`: Raw metadata items - `tech_stack`: Technologies mentioned in the job post - `tech_stack_visible`: Visible technologies (up to 4) - `tech_stack_more`: Indicator if more technologies are present - `raw_text`: Full raw text of the job posting - `actor_id`: Unique identifier for the actor run - `run_id`: Unique identifier for the run ### Error Handling Information The Actor is designed to handle common scraping errors gracefully. If an error occurs, it will log detailed information in the Apify console, allowing you to diagnose and resolve issues effectively. ### Rate Limiting and Best Practices To ensure optimal performance and avoid being blocked by Zoominfo: - Respect rate limits by configuring appropriate delays between requests. - Use the `item_limit` parameter to control data volume per run. - Monitor logs for any rate-limiting warnings and adjust settings accordingly. ### Limitations and Considerations - The Actor may not capture all job postings if they are behind authentication walls or require additional permissions. - Ensure compliance with Zoominfo's terms of service when using this tool. - Data accuracy is subject to the quality and availability of information on Zoominfo. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: [GetDataForMe Contact](https://getdataforme.com/contact/) We're here to help you get the most out of this Actor! # Actor input Schema ## `item_limit` (type: `integer`): Maximum items to scrape per actor run. Set to 0 for no limit. ## Actor input object example ```json { "item_limit": 10 } ``` # Actor output Schema ## `results` (type: `string`): Scraped data items from dataset # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/zoominfo-job-post-extractor").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 = {} # Run the Actor and wait for it to finish run = client.actor("getdataforme/zoominfo-job-post-extractor").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 '{}' | apify call getdataforme/zoominfo-job-post-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/zoominfo-job-post-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Zoominfo Job Post Extractor", "description": "The Zoominfo Job Post Extractor efficiently scrapes job postings, providing insights into hiring trends and technology stacks. It captures comprehensive data like company details, salary, and tech stack with high accuracy....", "version": "0.0", "x-build-id": "yYsjP3Hm5IkXux4Ho" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~zoominfo-job-post-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-zoominfo-job-post-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~zoominfo-job-post-extractor/runs": { "post": { "operationId": "runs-sync-getdataforme-zoominfo-job-post-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~zoominfo-job-post-extractor/run-sync": { "post": { "operationId": "run-sync-getdataforme-zoominfo-job-post-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "item_limit": { "title": "Item limit", "minimum": 0, "type": "integer", "description": "Maximum items to scrape per actor run. Set to 0 for no limit.", "default": 10 } } }, "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 } } } } } } } } } } ```