We extract job postings, salary bands, skill requirements, and employer profiles from worknz.co.nz. 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 worknz.co.nz. All fields typed and schema-versioned.
"job_id": "WNZ-89421", "title": "Senior Civil Engineer", "employer_name": "Fletcher Construction", "location": "Auckland CBD", "salary_min": 120000.0, "salary_max": 145000.0, "job_type": "Full-time", "posted_date": "2026-08-14T08:30:00Z", "remote_eligible": false
| # | job_id | title | employer_name | employer_id | location | region |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Salary Data objects from worknz.co.nz. All fields typed and schema-versioned.
"job_id": "WNZ-89421", "title": "Senior Civil Engineer", "salary_min": 120000.0, "salary_max": 145000.0, "currency": "NZD", "pay_period": "ANNUAL", "benefits_listed": "['Health Insurance', 'Company Vehicle']", "superannuation_included": true
| # | job_id | title | employer_name | salary_min | salary_max | salary_exact |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Employer Profiles objects from worknz.co.nz. All fields typed and schema-versioned.
"employer_id": "EMP-4029", "company_name": "Fletcher Construction", "industry": "Construction & Engineering", "company_size": "1000-5000", "headquarters": "Auckland", "active_jobs_count": 47, "founded_year": 1909
| # | employer_id | company_name | industry | website_url | company_size | headquarters |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Skill Requirements objects from worknz.co.nz. All fields typed and schema-versioned.
"job_id": "WNZ-89421", "skill_name": "AutoCAD", "experience_years": 5, "mandatory": true, "category": "Software", "normalized_skill": "autocad", "context_snippet": "Must have at least 5 years of commercial experience using AutoCAD."
| # | job_id | skill_name | experience_years | mandatory | category | normalized_skill |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from worknz.co.nz. All fields typed and schema-versioned.
"keyword": "civil engineer", "location_filter": "Auckland", "position": 3, "job_id": "WNZ-89421", "promoted": false, "employer_name": "Fletcher Construction", "scraped_at": "2026-08-15T10:12:44Z"
| # | keyword | location_filter | position | job_id | title | employer_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper handles every layer of the Worknz.co.nz platform: job listings, salary bands, employer directories, and search pagination — with anti-bot circumvention built in.
Title, full HTML description, job type, location, remote eligibility, and posting dates scraped accurately from individual job pages.
Extract stated salary ranges, exact figures, pay periods, and currency. We normalise text like '120k-145k + Super' into structured numeric fields.
Capture company name, industry, size, headquarters, and total active open roles directly from employer profile pages.
Parse unstructured job descriptions to isolate specific technical requirements, certifications, and years of experience requested.
Distinguish between on-site, hybrid, and fully remote roles across all New Zealand regions and specific postcodes.
Track organic vs promoted position for any job title search, monitoring which employers are aggressively bidding for visibility.
Monitor active URLs to detect exactly when a job is taken down, providing accurate time-to-fill metrics for the NZ market.
Run one-off bulk exports of historical jobs or configure continuous pipelines at hourly cadences with change-detection diffing.
Job boards deploy strict traffic shaping. We manage concurrency and proxy rotation to ensure zero IP bans and complete data extraction.
Brief in. Clean data out.
Provide job categories, locations, keywords, or specific employer names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for worknz.co.nz.
Schema validation, null-rate checks, salary-outlier detection, and sample listings before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Job boards deploy strict rate limits to protect their inventory. Here is how we stay resilient.
Worknz.co.nz uses traffic analysis to block scrapers. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management — trained on real applicant behaviour patterns.
Salary reveals and application modals are often JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering, capturing data that headless HTTP clients miss entirely.
Job boards frequently update their DOM structure. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, text-pattern matching, and structured data extraction (LD+JSON) — so a layout change does not break your data pipeline.
For large job catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs or status updates (e.g., job expired) — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, volume drops, schema drift, and coverage gaps — and respond before you notice. SLA uptime is contractual.
Economists and researchers track hiring volume, regional demand, and skill shifts across the New Zealand economy.
HR teams and compensation analysts aggregate salary bands to ensure their offers remain competitive in specific NZ regions.
Recruitment agencies and B2B software vendors monitor new job postings to identify companies actively expanding their teams.
Corporate strategy teams monitor competitor job postings to deduce strategic direction, technology stack choices, and expansion plans.
Niche job boards and career portals ingest Worknz.co.nz listings to backfill their own inventory and provide comprehensive search.
Hedge funds and quantitative analysts correlate job posting velocity with corporate health and macroeconomic trends.
"Worknz.co.nz holds the pulse of the New Zealand labor market — but building a reliable feed requires navigating rate limits and shifting DOM structures."
Most teams underestimate the investment required: reliable job board scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our worknz.co.nz 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 crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across New Zealand and Australia regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About worknz.co.nz scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings is generally permissible under applicable law, reinforced by rulings like hiQ v. LinkedIn. DataFlirt targets only public, non-authenticated job and employer data. We do not extract personal candidate data or circumvent authentication walls. Clients should review terms of service and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human applicant behaviour. Our selectors have multi-layer fallback chains so DOM changes do not break the pipeline. We monitor for rate limits in real time and trigger pool rotation automatically.
Yes. While we extract structured salary fields directly from the metadata, our parsing engine also uses regex and NLP to identify and extract numeric salary bands and pay periods mentioned within the raw HTML job description.
Real-time streaming pipelines achieve sub-60-minute latency for new job postings based on specific keyword or category alerts. Full site refreshes at daily cadence complete within a 4-8 hour window depending on total volume.
Yes. For ongoing pipelines, we maintain state of all active job URLs. If a URL returns a 404 or an 'expired' tag, we emit an update record marking the job as closed, allowing you to calculate precise time-to-fill metrics.
Our smallest packages start at a defined set of categories or keywords with weekly delivery. For full-site extraction or custom schema requirements, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 500 job listings or 50 search result pages as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off regional extraction or a continuous feed of all New Zealand job postings — we scope, build, and operate the pipeline. Tell us what you need.
