You connect vacancy data to your own recruitment platform by integrating external job sources through APIs, structured data feeds, or web scraping pipelines that pull listings into your system automatically. The right approach depends on the sources you need to cover, the freshness requirements of your data, and the technical capabilities of your team. Openindex covers the full range of these methods in detail below, from foundational concepts to practical integration decisions.
What types of vacancy data sources are available for recruitment platforms?
Vacancy data for recruitment platforms comes from four main source types: job board APIs, employer career pages, aggregator feeds, and applicant tracking systems (ATS) that publish open positions. Each source type varies in structure, update frequency, and accessibility, which directly shapes how you integrate it into your platform.
Job board APIs from platforms like LinkedIn, Indeed, or national job portals offer structured, well-documented data that is relatively straightforward to consume. Employer career pages, on the other hand, are unstructured and require crawling or scraping to extract vacancy information. Aggregator feeds compile listings from multiple sources into a single stream, which reduces integration complexity but may introduce duplication. ATS integrations give direct access to an employer’s live vacancy inventory, making them the most accurate source for specific clients or partners.
For recruitment platforms aiming to cover a broad market, a combination of these sources is almost always necessary. A single source rarely provides the coverage, depth, or geographic spread that a competitive platform needs in 2026.
How does a vacancy API work for recruitment platforms?
A vacancy API works by exposing job listing data through a set of HTTP endpoints that your recruitment platform queries to retrieve, filter, and display positions. Your platform sends a request with parameters such as job title, location, or date posted, and the API returns a structured response, typically in JSON or XML, containing the matching vacancy records.
From an integration perspective, your development team authenticates with the API provider, maps the returned data fields to your own data model, and schedules regular calls to keep your listings current. Most vacancy APIs support pagination for large result sets and webhooks or delta endpoints for incremental updates, so you only fetch new or changed records rather than pulling the entire dataset on every call.
The quality of a vacancy API depends on how consistently the provider structures its data, how frequently it updates it, and how well its documentation covers edge cases. When evaluating a vacancy API for your recruitment platform, pay close attention to rate limits, data licensing terms, and whether the API covers the specific sectors or regions your audience needs.
What’s the difference between a job feed and web scraping for vacancy data?
A job feed is a pre-structured data stream, often in XML or JSON format, that a job board or employer provides intentionally for distribution. Web scraping is the automated extraction of vacancy data directly from web pages when no official feed exists. The key difference is consent and structure: feeds are designed to be consumed, while scraping retrieves data that was published for human readers.
Job feeds: structured and sanctioned
Job feeds follow a defined schema, which makes parsing straightforward and reliable. Providers maintain them as a distribution channel, so the data is generally clean, consistently formatted, and updated on a predictable schedule. Integration is faster, and the legal basis for using the data is clear because the provider explicitly shares it.
Web scraping: flexible but more complex
Web scraping becomes necessary when a vacancy source does not offer a feed or API. A scraper navigates the target website, extracts the relevant fields, and structures the raw HTML into usable recruitment data. This approach covers a far wider range of sources but requires ongoing maintenance as websites change their layouts. It also demands careful attention to the terms of service of each site and applicable data regulations such as the GDPR, which governs how personal data embedded in vacancy listings may be processed.
For most recruitment platforms, the practical answer is to use feeds wherever they exist and rely on scraping to fill the gaps where no official data channel is available.
How do you keep vacancy data up to date in real time?
Keeping vacancy data current requires a combination of scheduled polling, incremental update mechanisms, and expiry logic that removes filled or expired positions. True real-time synchronization is rarely achievable across all sources, but a well-designed pipeline can deliver near-real-time freshness for the sources that matter most to your users.
For API-based sources, use delta endpoints or webhook notifications where available. These push only new or changed records to your system rather than requiring a full data refresh, which reduces both latency and server load. For scraped sources, crawl frequency should match the typical posting velocity of each site. A high-volume job board may warrant hourly crawls, while a niche employer career page might only need daily checks.
Equally important is expiry handling. Vacancy data ages quickly, and listings that are no longer active degrade the user experience on your platform. Build logic that checks the status of existing records at regular intervals and removes or flags positions that have been taken down. Combining proactive updates with reactive expiry management is the most reliable way to maintain a clean, current job database.
What technical requirements does vacancy data integration need?
Vacancy data integration requires a data ingestion layer, a normalization process, a storage solution suited to search and filtering, and a deduplication mechanism. Together, these components turn raw data from multiple heterogeneous sources into a consistent, queryable dataset your recruitment platform can serve to users.
The ingestion layer handles the connection to each source, whether that is an API client, a feed parser, or a web crawler. Normalization maps incoming fields to a unified schema so that a job title field from one source aligns with the equivalent field from every other source. Storage should support full-text search and faceted filtering, which is why search engines built on Apache Solr or Elasticsearch are common choices for recruitment platforms handling large volumes of aggregated job data.
Deduplication deserves particular attention when you pull from multiple sources. The same vacancy often appears on several job boards simultaneously. Without deduplication logic based on employer, title, location, and posting date, your platform will surface the same position multiple times, which frustrates users and inflates your apparent listing count. A robust matching algorithm or fingerprinting approach is essential once your source count grows beyond a handful of feeds.
Should you build vacancy data collection in-house or use a managed service?
Whether to build vacancy data collection in-house or use a managed service depends on your team’s technical capacity, the number of sources you need to cover, and how much of your development budget you want to allocate to infrastructure rather than product features. For most recruitment platforms, a managed service delivers a better return on investment, especially in the early stages.
Building in-house gives you full control over data models, crawl schedules, and source selection. It is the right choice when your vacancy data requirements are highly specific, when you have existing infrastructure expertise, or when the competitive advantage of your platform depends on proprietary data collection methods. The trade-off is ongoing engineering effort: scrapers break when websites change, APIs deprecate endpoints, and feeds shift their schemas.
A managed crawling or data service offloads that maintenance burden entirely. You receive clean, structured vacancy data delivered via API or feed, and the provider handles source monitoring, legal compliance, and infrastructure scaling. This model suits platforms that want to focus engineering resources on the recruitment product itself rather than the data pipeline underneath it.
How Openindex helps with vacancy data integration
We specialize in exactly the kind of data collection and integration challenges that recruitment platforms face. Whether you need a reliable vacancy API, a managed crawling pipeline, or a complete job data aggregation solution, we build and maintain it for you so your team can focus on your core product. Here is what we offer:
- Crawling as a Service: We handle the full crawling process across your chosen vacancy sources, delivering clean, structured data without you managing a single scraper.
- Data as a Service: Receive normalized, deduplicated recruitment data as a feed or directly integrated into your application via API.
- Custom vacancy API development: We build tailored API layers that map to your data model and scale with your platform’s growth.
- Search infrastructure: Using Apache Solr, Elasticsearch, and related open-source technologies, we set up search and filtering backends capable of handling millions of vacancy records.
- GDPR-compliant data collection: Our processes are designed with Dutch and European data regulations in mind, so your platform stays on the right side of compliance.
If you are building or scaling a recruitment platform and want to connect reliable vacancy data without the overhead of managing the pipeline yourself, contact us to discuss what a tailored solution looks like for your situation.
Frequently Asked Questions
How long does it typically take to integrate a vacancy API into an existing recruitment platform?
For a well-documented API with clean data, a basic integration can be completed in a few days to a couple of weeks, depending on your team's familiarity with the data model and how much normalization is required. More complex setups involving multiple sources, custom deduplication logic, or search infrastructure can take several weeks. Starting with a single high-quality source and expanding from there is usually the most practical approach.
What is the most common mistake recruitment platforms make when aggregating vacancy data?
The most common mistake is underestimating deduplication. When pulling from multiple job boards and feeds, the same vacancy frequently appears across several sources simultaneously, which inflates listing counts and frustrates users. Building a solid matching algorithm based on employer, job title, location, and posting date early in the process saves significant cleanup effort later.
Is web scraping vacancy data legally allowed under GDPR?
It depends on what data is being collected and how it is used. Publicly posted vacancy information is generally not personal data, but listings that include recruiter contact details or other identifiable information fall under GDPR rules. You should always review the terms of service of each site you scrape and ensure your data handling processes are compliant with applicable regulations before going live.
When does it make sense to switch from a managed vacancy data service to an in-house solution?
Switching in-house typically makes sense when your platform has grown to a point where proprietary data collection becomes a genuine competitive differentiator, or when your specific source and schema requirements fall outside what a managed service can accommodate. For most platforms, the ongoing engineering cost of maintaining scrapers and API integrations outweighs the benefits of full ownership unless data infrastructure is central to your product strategy.
