We extract job listings, salary signals, recruiter profiles, and employer data from CV-Library. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Job Postings objects from cv-library.co.uk. All fields typed and schema-versioned.
"job_id": "21849201", "title": "Senior Python Developer", "company_name": "TechCorp UK", "location": "London", "salary_min": 75000.0, "salary_max": 90000.0, "salary_type": "per annum", "remote_flag": true, "job_type": "Permanent"
| # | job_id | title | company_name | location | salary_min | salary_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Salary Data objects from cv-library.co.uk. All fields typed and schema-versioned.
"job_id": "21849201", "title": "Senior Python Developer", "salary_raw": "£75,000 - £90,000/annum + Bonus", "salary_min": 75000.0, "salary_max": 90000.0, "currency": "GBP", "period": "annual", "benefits": "Bonus, Pension, Healthcare"
| # | job_id | title | sector | salary_raw | salary_min | salary_max |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Company Profiles objects from cv-library.co.uk. All fields typed and schema-versioned.
"company_id": "C9842", "name": "TechCorp UK", "industry": "Information Technology", "active_jobs_count": 14, "headquarters": "London, UK", "website": "https://techcorp.co.uk", "size": "501-1000"
| # | company_id | name | industry | description | logo_url | website |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Recruiter Intelligence objects from cv-library.co.uk. All fields typed and schema-versioned.
"recruiter_id": "R4921", "name": "Sarah Jenkins", "agency_name": "TechTalent Partners", "total_postings": 42, "contact_phone": "+44 20 7946 0958", "sector_focus": "Software Engineering", "agency_url": "https://cv-library.co.uk/agency/techtalent"
| # | recruiter_id | name | agency_name | agency_url | total_postings | contact_phone |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from cv-library.co.uk. All fields typed and schema-versioned.
"keyword": "data engineer", "location": "Manchester", "position": 3, "job_id": "21849555", "sponsored_flag": false, "salary_preview": "£60,000/annum", "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | location | radius | position | job_id | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our pipeline handles the complexities of job board scraping, including pagination limits, location normalisation, salary text parsing, and bot mitigation.
Capture title, raw HTML description, job type, location, and application URLs for every listing on the platform.
Parse unstructured salary strings into min, max, currency, and period fields for direct quantitative analysis.
Identify whether a listing is posted by a recruitment agency or a direct employer, including agency contact details.
Extract and normalise location data, including remote work flags and regional categorisation.
Monitor active URLs to detect exactly when a job is removed, providing precise time-to-fill metrics.
Track organic and sponsored search positions for specific job titles across target UK postcodes.
Extract employer descriptions, active job counts, and metadata from dedicated company pages.
Maintain a hash index of active jobs. Subsequent runs only push new listings or status changes.
Configure continuous pipelines at hourly or daily cadences to capture fast-moving contract roles.
Brief in. Clean data out.
Provide target keywords, sectors, locations, or specific employer IDs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and parsing logic specific to CV-Library DOM structures.
Schema validation, null-rate checks, and salary parsing accuracy verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Job boards employ strict rate limits and pagination caps. Here is how our infrastructure guarantees complete data capture.
CV-Library limits search results to a fixed number of pages. We bypass this by programmatically bisecting searches using granular location radii and salary bands to ensure 100% capture of large categories.
Salary data is often entered as free text (e.g., '£45k - £50k + OTE'). Our parsing layer uses compiled regex patterns to extract absolute numeric values, standardise currencies, and normalise time periods.
We route requests through UK-based residential ISP proxies to avoid geographic blocking and rate-limiting heuristics common to major UK job boards.
For large daily runs, we maintain a state store of all active job IDs. The pipeline only emits net-new jobs, modified listings, and expired flags, reducing downstream processing costs.
We use multiple fallback chains for critical fields like salary and job type, ensuring that minor A/B tests on the CV-Library frontend do not break your data feed.
Economic researchers and hedge funds track hiring volume across UK regions and sectors as a leading indicator of economic health.
HR platforms aggregate real-time salary bands by job title and location to provide accurate compensation guidance to employers.
SaaS companies identify businesses actively hiring for specific roles (e.g., hiring a CRM manager signals intent to buy CRM software).
Enterprise talent teams monitor competitor hiring velocity and strategic role openings to anticipate product roadmaps.
Niche job boards backfill their inventory by programmatically importing relevant listings from major generalist boards.
Agencies track which direct employers are struggling to fill roles, identifying high-probability targets for their services.
"CV-Library holds one of the most comprehensive datasets of UK hiring intent and salary trends, but extracting it reliably requires bypassing strict pagination caps and parsing highly unstructured text."
Building a scraper for a major job board is trivial. Maintaining it at scale is not. Rate limits, layout changes, and complex text parsing require constant engineering attention. DataFlirt manages the entire extraction lifecycle, delivering clean, query-ready data so your team can focus on analytics rather than maintenance.
Everything supported by our cv-library.co.uk scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles high-throughput crawl orchestration, deduplication, and retry logic, optimising for speed on static HTML pages.
We maintain pools of UK residential ISP proxies. Rotation happens per-request to distribute load and avoid IP reputation degradation.
Pipelines run on Kubernetes. Airflow handles scheduling and dependency management. All state is stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About cv-library.co.uk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job listings is generally permissible. DataFlirt targets only public, non-authenticated job and company data. We do not extract personal candidate data or CVs. Clients should review terms of service and consult legal counsel for specific use cases.
No. Candidate CVs are gated behind employer logins and subject to strict data protection regulations (GDPR). We only extract public job postings and employer/agency metadata.
Our parsing engine uses regular expressions to identify currency symbols, numeric ranges, and time periods (e.g., hourly, annual). We output both the raw string and the normalised min/max numerical values.
Pipelines can be configured to run hourly for specific high-priority searches, or daily for full category sweeps. Incremental runs complete quickly by only checking known active URLs and new search pages.
We maintain a state database of all active job URLs. During a run, we verify the HTTP status and page content of known URLs. If a listing redirects or displays an expired notice, we flag it as closed in the output.
Our smallest packages start at a defined set of search parameters or categories with daily delivery. We price based on data volume, extraction complexity, and delivery frequency.
Yes. We provide a sample run of up to 1,000 job records as part of the scoping process. This allows you to validate our salary parsing accuracy and schema fit before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of specific tech roles or a full sweep of the UK job market, we build and operate the infrastructure. Tell us what you need.
