Raw job data from employer career pages is almost always incomplete, inconsistent, and unstructured. Most job boards accept this as normal. Here is what it actually costs, and what job data enrichment fixes.
You know the listings. The ones with no location. The title says “Sr. Dev.” The salary field that reads “competitive.” The role that was filled three weeks ago but is still live on your board because the employer forgot to take it down.
Every job board has them. Most treat them as an acceptable byproduct of operating at scale.
They are not. They are a measurable, avoidable drag on search quality, job seeker trust, SEO performance, and recruiter satisfaction. And they compound silently over time.
This is what raw job data costs. And this is what job data enrichment fixes.
What “Raw Job Data” Actually Means
Direct answer: Raw job data is job listing information collected directly from employer career pages before any cleaning, normalisation, or enrichment has been applied. It typically contains incomplete fields, inconsistent formatting, duplicate records, and expired listings that no longer reflect open roles.
When an employer publishes a job on their career page, the data is written for human readers, not for ingestion into a structured database. There is no standard field format, no required salary disclosure, no universal location convention. Every employer does it differently.
What comes back when you crawl that page is raw data. Sometimes it is nearly complete. More often it is not.
The Six Fields That Break Most Often
Based on Propellum’s analysis of over one billion job records processed across 15+ countries, these are the fields that are most frequently incomplete, incorrect, or missing entirely in raw job data.
| Field | Problem in raw data | What it costs your board |
|---|---|---|
| Job title | Abbreviations, internal codes, non-standard naming (“SR ENG III”) | Breaks search filters, mismatches user queries, reduces relevance |
| Location | Vague strings (“NYC area,” “hybrid,” “office”) | Kills radius search, breaks geographic filtering, misleads job seekers |
| Salary | Missing in approximately 70% of postings globally | Reduces job seeker engagement. Salary-visible listings receive significantly higher apply rates |
| Skills | Buried in free-text descriptions, not extracted | Cannot be used for skills-based matching or personalisation |
| Employment type | Often missing or inconsistent (“FT,” “Full-time,” “Permanent”) | Filters fail; job seekers cannot segment by contract type |
| Expiry status | Employer forgets to remove filled roles | Dead listings damage SEO crawl budget, frustrate job seekers, erode board trust |
This is not an edge case. At any given moment, a job board operating on raw data has a significant percentage of listings that are incomplete, duplicated, or expired. The exact share varies by source. Across Propellum’s client base, boards that ran on raw data before switching to enriched feeds consistently found their structured data completeness below 60%.
What Incomplete Data Actually Costs
1. Search quality degrades immediately
A job board’s search is only as good as its data. When titles are inconsistent, “Senior Software Engineer,” “Sr. SWE,” and “Software Engineer III” return as three separate search results. Or worse, as no results when the job seeker types a query none of them match.
When locations are vague, radius search returns nothing useful. When salary is missing, salary-filtered searches exclude the listing entirely.
Job data enrichment solves this by normalising every field into a consistent, structured taxonomy before the record reaches your index. One title standard. One location format. Every listing is classifiable.
2. SEO takes a quiet, compounding hit
Search engines crawl job boards. They evaluate page quality. Expired listings, duplicate postings, and thin content are exactly the signals that suppress crawl budgets and reduce the authority of your job pages in organic rankings. These are the hallmarks of unenriched raw data.
A board with 100,000 listings where 20% are expired or duplicated has effectively created 20,000 low-quality pages that dilute the SEO value of the remaining 80,000.
Structured job data, where every record is complete, unique, and current, is also more indexable job data. The board with cleaner data ranks for more queries with less effort.
3. Job seekers leave and don’t come back
The job seeker who clicks on a role that turns out to be filled, based in the wrong city, or missing information they need to decide whether to apply will not click again. They navigate away. On mobile, they delete the app.
Trust in a job board is built listing by listing. A single bad experience does not end a relationship, but a pattern of them does. And raw data creates patterns.
4. Salary absence is a specific, solvable problem
Approximately 70% of job postings globally do not include salary information at point of posting. This is well-documented. What is less discussed is the cost of accepting it.
Job data enrichment with salary and skills addresses this directly. AI models trained on market data can estimate salary ranges from role, seniority, location, and industry. They fill the field where the employer left it blank. The result is a listing that is more useful to the job seeker and more competitive for the board.
Skills extraction applies the same logic to the skills field. Rather than requiring the employer to tag their listing with structured skills data, which most do not do, NLP models read the free-text description and extract the skills the role requires. Those extracted skills power matching, recommendations, and personalisation features that raw data cannot support.
What Job Data Enrichment Actually Does
Direct answer: Job data enrichment is the process of transforming raw, incomplete job listing data into structured, accurate, standardised records. It uses AI and natural language processing to fill missing fields, normalise inconsistent values, remove duplicates, detect expired listings, and map extracted data to a consistent taxonomy before delivery to a job board.
AI job data processing applies across six enrichment layers:
Title normalisation: maps any job title variant to a standard taxonomy
Location parsing and geocoding: converts any location string to structured, searchable coordinates
Salary estimation: fills missing salary fields using market benchmarking
Skills extraction: reads free-text descriptions and outputs structured skills tags
Deduplication: identifies and consolidates identical listings across sources
Expiry detection: monitors source career pages and flags closed roles for removal
The output is a complete, consistent, structured job record, ready to be indexed, searched, matched, and served to job seekers without transformation on your end.
Before and After: What Enrichment Looks Like in Practice
| Field | Raw data | After enrichment |
|---|---|---|
| Title | Sr. Dev | Senior Software Engineer |
| Location | NYC office | New York, NY 10001 (40.7128, -74.0060) |
| Salary | Not disclosed | $140,000 – $175,000 |
| Skills | Not listed | Python, React, AWS, Agile, System Design |
| Employment type | FT | Full-time |
| Status | Listed | Active, verified live as of crawl |
The difference is not cosmetic. The enriched record powers search filters the raw record cannot. It appears in salary-filtered searches the raw record is excluded from. It surfaces in skills-matching workflows the raw record is invisible to.
The Bottom Line
Raw job data is not a minor inconvenience. It is a structural problem that affects search quality, job seeker experience, SEO rankings, and the competitive position of a job board that depends on all three.
Job data enrichment is what converts raw, inconsistent, incomplete records into structured job data that a job board can actually use, at the level of quality that job seekers expect and that modern search requires.
Propellum has been running job data enrichment pipelines for the world’s leading job boards for over 25 years, processing more than a billion job records. The boards that scale fastest are the ones that solved the data quality problem first.
See what enriched job data looks like for your board. Request a free sample →
Raw job data is collected directly from employer career pages without processing. It contains whatever the employer wrote, in whatever format they used. Structured job data has been normalised, validated, and enriched so that every field follows a consistent format, missing values have been filled where possible, duplicates have been removed, and expired listings have been flagged.
AI job data processing uses natural language processing models to read free-text job descriptions and extract structured data: job titles, required skills, location, seniority level, employment type. These are fields the employer did not provide in a machine-readable format. Models are trained on large datasets of job records to recognise patterns and extract consistently. The output is a structured record that can be indexed and searched.
Most employers do not disclose salary at the point of posting, either by choice or policy. Some countries and US states are introducing salary transparency legislation that is changing this. But globally, salary absence remains the norm. Job data enrichment addresses this by estimating salary ranges from role type, seniority, location, and industry benchmarks drawn from market data.
Job data enrichment with salary and skills includes salary range estimation for postings where the employer has not disclosed pay, NLP-based extraction of required and preferred skills from job description text, skills taxonomy mapping to a standardised skill set, and seniority classification. These enriched fields directly power salary-filtered search, skills-based job matching, and personalised job recommendations.
In a real-time pipeline, enrichment occurs immediately after a new job is detected on the source career page, typically within minutes of the employer posting. The enriched record is delivered to the job board’s index without manual delay. There is no batch processing window. Expiry detection also runs continuously, not on a fixed schedule.
Yes. Enrichment can be applied to a historical index as a backfill operation, as well as to incoming data in real time. Propellum has run backfill enrichment across legacy job board databases, normalising historical records that were ingested without enrichment and bringing them to the same standard as incoming data.
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