Job Board Aggregation for Technical Teams: schema.org, JDX, JobsMCP
Job board aggregation means pulling listings from multiple sources, such as corporate career pages and job boards, into one searchable, standardised feed. It relies on three main ingestion methods: structured feeds and APIs, web scraping, and direct career-page parsing. The result is broader coverage, easier filtering, and flexible delivery options for whoever consumes the data next.
TL;DR:
- Prefer structured feeds or APIs when available; HTML scraping reaches otherwise inaccessible listings but needs ongoing engineering as layouts, bot defenses, and rate limits change.
- schema.org JobPosting and JDX can reduce custom field mapping, but voluntary adoption and inconsistent values still leave location formats and other fields unreliable.
- Pilot one or two feeds before building or signing a vendor contract, then compare engineering, hosting, proxy, compliance, and ongoing scraping maintenance costs.
- Evaluate deduplication using canonical IDs plus fuzzy title, company, and location matching, alongside freshness rules, documented rate limits, expiry policies, and compliance terms.
Table of Contents
- How the aggregation pipeline turns scattered listings into one feed
- Why data standards matter for matching and discoverability
- Should you build an aggregator in-house or buy one?
- What to check when evaluating an aggregator's features
- How Appliqu fits the aggregation checklist
- Where aggregation is heading next
- Try Appliqu for automated job board coverage
- FAQ
- Sources
How the aggregation pipeline turns scattered listings into one feed
Aggregators draw from three broad source types, each with its own quirks. Job boards publish large volumes but often restrict bulk access. Corporate career pages offer first-party accuracy but inconsistent formatting. Applicant tracking system (ATS) feeds, where available, tend to give the cleanest structured data because they come directly from the hiring workflow.
The ingestion method we choose shapes everything downstream:
- Structured feeds or APIs give consistent fields and the lowest ongoing maintenance, when a provider offers them.
- RSS/XML feeds sit between structured APIs and raw scraping: easier to parse than HTML, but less flexible than a proper API.
- HTML scraping reaches sources with no feed at all, but breaks whenever a page layout changes.
Once collected, raw listings need parsing and normalisation: mapping inconsistent field names to a canonical schema, assigning stable job identifiers so the same vacancy isn't counted twice, and handling timestamps consistently so "posted today" means the same thing across sources. None of this is trivial at scale. Operators also contend with bot defences, rate limits that throttle how quickly a feed can be polled, and selectors that quietly stop working after a site redesign, which is why scraping-heavy pipelines need ongoing engineering attention rather than a one-off build.
Why data standards matter for matching and discoverability
Standardisation is what separates a reliable aggregator from a fragile one. The widely used schema.org JobPosting format structures job adverts so search engines and aggregators can read them consistently, though implementers vary in which property values they actually populate, which limits how far adoption alone can solve the problem.
JDX, discussed by the HR Open Standards Consortium, and related efforts such as JobSchema+ aim to harmonise fields across systems further than schema.org does alone. Adoption remains voluntary and uneven, so treat these as a direction of travel rather than a settled standard. On the interface side, projects such as JobsMCP show an emerging approach: a defined API surface with functions for searching jobs, retrieving job detail, and pulling market rates, built so external systems can query one stable interface instead of scraping fragile pages.
A 2015 UK government data panel review found that adopting a standard schema reduces bespoke field mapping, but flagged that property values, such as job location formats, still need further standardisation to be fully reliable, a gap that persists in practice today.
Practical steps worth taking before committing to any source:
- Favour partners who expose schema.org JobPosting or JDX fields rather than raw HTML.
- Ask vendors for a JSON or CSV schema sample before integration, not after.
Should you build an aggregator in-house or buy one?
The decision comes down to control versus speed. Building in-house makes sense when you need proprietary coverage, custom matching logic, or deep control over how listings are enriched after ingestion. Buying makes sense when speed to market, lower ongoing maintenance, and a commercial support arrangement matter more than bespoke control.
Before committing either way, work through these steps:
- List your coverage requirements: which sources must you reach, and do any of them already expose structured feeds?
- Estimate the cost buckets: engineering hours, proxy and hosting costs, deduplication logic, and legal or compliance review.
- Run a scoped pilot: integrate one or two feeds first rather than committing to a full build or a full vendor contract.
- Compare maintenance burden: scraping-heavy builds need continuous upkeep; feed-based integrations need far less.
Pro Tip: Pilot with a single scoped feed before signing a full vendor contract or writing a full scraper; a small pilot exposes schema gaps and maintenance costs you won't see on a vendor's feature sheet.
What to check when evaluating an aggregator's features
Whether you're assessing a vendor or scoping a build, the acceptance criteria look similar. Deduplication deserves particular scrutiny: a commercial job search service listed on a UK government digital marketplace describes combining canonical identifiers where present with fuzzy-text similarity on title, company, and location, plus a freshness or source-priority rule to pick the canonical record when duplicates appear.
Beyond deduplication, check for:
- Delivery flexibility: REST or GraphQL APIs, webhooks, and bulk exports, each with documented rate limits.
- Freshness handling: clear timestamps and a defined policy for marking stale listings as expired.
- Matching and enrichment: semantic matching, skill tagging, salary normalisation, and ATS-ready output formatting.
- Operational controls: monitoring, error handling, and a documented data processing agreement for GDPR compliance.
| Checklist area | What good looks like |
|---|---|
| Deduplication | Canonical IDs plus fuzzy matching on title, company, location |
| Delivery | API, webhook, and bulk export options with documented rate limits |
| Freshness | Explicit timestamps and expiry rules |
| Compliance | A documented data processing agreement |
The same digital marketplace listing notes that production aggregators typically combine these technical features with business-facing ones, such as management information dashboards and fraud detection, since the same pipeline often serves both jobseekers and employers.
How Appliqu fits the aggregation checklist
Appliqu searches job boards continuously and applies semantic matching, reading the full job description rather than relying on keyword overlap alone to find suitable roles. Mapped against the checklist above, Appliqu tailors a CV and cover letter for each application, produces ATS-friendly CV formatting, and keeps searching across more than 100 job boards around the clock rather than on a fixed schedule.
On pricing, the facts are straightforward: Explorer is a permanent free tier, and Appliqu writes five applications on it. Sending starts with Core at 29.90 EUR per month (19% VAT included) for 100 applications. Pro, at 69.90 EUR per month for 300 applications, adds access to the Appliqu Job Board. Concierge, at 199.00 EUR per month, includes unlimited applications and a senior consultant session.
If you're evaluating Appliqu or a comparable option, ask for:
- The current list of covered job boards.
- Documentation of the application programming interface or data schema used.
- The data processing agreement covering GDPR compliance.
Where aggregation is heading next
Structured feeds and richer APIs are likely to keep gaining ground over scraping, as more sources recognise the engineering savings that schema.org, JDX, and interface projects like JobsMCP offer. If you're starting out, run a small feed pilot and request schema examples and sample data from any vendor before committing. Track success by watching duplicate listings fall, location fields tidy up, and freshness become more reliable.
Alex
Try Appliqu for automated job board coverage
Appliqu brings the checklist above into one continuous process: searching over 100 job boards, tailoring each application with semantic matching, and producing ATS-ready documents without manual re-entry.
Explorer gives you five free applications to see the process firsthand, and sending begins with Core at 29.90 EUR per month for 100 applications. Visit Appliqu's plans page to compare Core, Pro, and Concierge and pick the one that matches how many applications you need to send.
FAQ
What is job board aggregation?
Job board aggregation is the process of collecting job listings from multiple sources, such as job boards, corporate career pages, and applicant tracking systems, into one searchable, standardised feed. It typically relies on structured feeds, APIs, or web scraping to gather listings, followed by normalisation to make fields consistent across sources.
How do aggregators avoid showing duplicate job listings?
Deduplication typically combines canonical job identifiers where they exist with fuzzy-text matching on fields like title, company, and location, as described in a commercial job search service's feature listing. A freshness or source-priority rule then decides which version of a duplicate listing becomes the canonical record.
What is schema.org JobPosting and why does it matter?
schema.org JobPosting is a widely used format for structuring job advert data so search engines and aggregators can read it consistently. Adoption is broad, but implementers vary in which specific fields they populate, which is why JDX and related harmonisation efforts aim to tighten consistency further.
Should a business build its own job aggregator or buy one?
Building in-house suits businesses that need proprietary coverage or custom matching logic and have the engineering capacity for ongoing maintenance. Buying suits those who prioritise speed to market and lower maintenance, with a scoped pilot integration recommended before committing fully either way.
What does Appliqu cost and what do the plans include?
Explorer is a free tier where Appliqu writes five applications. Sending starts with Core at 29.90 EUR per month for 100 applications, Pro costs 69.90 EUR per month for 300 applications plus the Appliqu Job Board, and Concierge costs 199.00 EUR per month for unlimited applications plus a senior consultant session, all prices including 19% VAT.
Sources
- Guest Post: JDX: a schema for Job Data Exchange - HR Open Standards Consortium, Inc.
- jobsmcp/JobsMCP
- Adzuna Job Search, Job Board and Skills Matching Solution - Digital Marketplace