# eFinancialCareers Jobs Scraper (`codingfrontend/efinancialcareers-jobs-scraper`) Actor A fast, high-performance scraper built for lead generation, competitor analysis, and business intelligence. Extracts detailed business profiles, including name, contact details, emails, phone numbers, location, ratings, and reviews from Efinancialcareers Jobs. Supports custom search URLs, proxy r... - **URL**: https://apify.com/codingfrontend/efinancialcareers-jobs-scraper.md - **Developed by:** [Coding Frontned](https://apify.com/codingfrontend) (community) - **Categories:** Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $4.99 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## eFinancialCareers Jobs Scraper Scrape finance, banking, and investment job listings from eFinancialCareers.com. Uses the official search API for reliable access to thousands of jobs. ### Features - Search by keyword + location - Returns job title, company, salary, location, employment type, sectors - Full pagination support (1,000+ jobs per search) - No proxy required ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `query` | string | — | Job keyword (e.g. `analyst`, `quant`, `compliance`) | | `location` | string | — | Location (e.g. `london`, `new york`, `singapore`) | | `maxItems` | integer | 50 | Max jobs to return | | `culture` | string | `en` | Language/locale | | `currencyCode` | string | `USD` | Salary currency | ### Output Fields | Field | Description | |-------|-------------| | `jobId` | eFinancialCareers numeric ID | | `title` | Job title | | `jobUrl` | URL to job detail page | | `companyName` | Employer name | | `sector` / `sectors` | Finance sector tags | | `employmentType` | Full time / Part time | | `locationDisplay` | City, Country | | `salary` | Salary as shown (e.g. "Competitive", "£70,000-£90,000") | | `salaryMin` / `salaryMax` | Numeric salary range when available | | `salaryCurrency` | Currency code | | `positionType` | Senior/Junior/Graduate level | | `summary` | Job description excerpt | | `postedDate` | ISO date of posting | | `scrapedAt` | ISO timestamp | ### Notes - Uses the eFinancialCareers JSON search API directly — no browser needed. - Supports up to 136 pages (25 jobs/page) for most searches. - No authentication required. # Actor input Schema ## `query` (type: `string`): Job title, skill, or keyword (e.g. 'analyst', 'quantitative finance', 'risk management'). ## `location` (type: `string`): City, country, or region (e.g. 'london', 'new york', 'singapore'). ## `maxItems` (type: `integer`): Maximum number of job listings to return. ## `culture` (type: `string`): Language/locale code for results. ## `currencyCode` (type: `string`): Currency for salary display (e.g. USD, GBP, EUR, SGD). ## Actor input object example ```json { "query": "quantitative analyst", "location": "london", "maxItems": 50, "culture": "en", "currencyCode": "USD" } ```` # 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("codingfrontend/efinancialcareers-jobs-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 = {} # Run the Actor and wait for it to finish run = client.actor("codingfrontend/efinancialcareers-jobs-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 '{}' | apify call codingfrontend/efinancialcareers-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=codingfrontend/efinancialcareers-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "eFinancialCareers Jobs Scraper", "description": "A fast, high-performance scraper built for lead generation, competitor analysis, and business intelligence. Extracts detailed business profiles, including name, contact details, emails, phone numbers, location, ratings, and reviews from Efinancialcareers Jobs. Supports custom search URLs, proxy r...", "version": "0.1", "x-build-id": "fCVfEwmzz6udVuLP6" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/codingfrontend~efinancialcareers-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-codingfrontend-efinancialcareers-jobs-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/codingfrontend~efinancialcareers-jobs-scraper/runs": { "post": { "operationId": "runs-sync-codingfrontend-efinancialcareers-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/codingfrontend~efinancialcareers-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-codingfrontend-efinancialcareers-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "query": { "title": "Search Query", "type": "string", "description": "Job title, skill, or keyword (e.g. 'analyst', 'quantitative finance', 'risk management')." }, "location": { "title": "Location", "type": "string", "description": "City, country, or region (e.g. 'london', 'new york', 'singapore')." }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of job listings to return.", "default": 50 }, "culture": { "title": "Culture", "type": "string", "description": "Language/locale code for results.", "default": "en" }, "currencyCode": { "title": "Currency Code", "type": "string", "description": "Currency for salary display (e.g. USD, GBP, EUR, SGD).", "default": "USD" } } }, "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 } } } } } } } } } } ```