We extract job postings, salary estimates, company profiles, and location data from ZipRecruiter. 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 ziprecruiter.com. All fields typed and schema-versioned.
"job_id": "ZR_938472", "title": "Senior Data Engineer", "company": "TechCorp", "location": "Austin, TX", "salary_range": "$120,000 - $160,000", "job_type": "Full-Time", "remote_status": "Hybrid"
| # | job_id | title | company | location | salary_range | job_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Salary Estimates objects from ziprecruiter.com. All fields typed and schema-versioned.
"job_title": "Data Engineer", "location": "Austin, TX", "min_salary": 115000, "max_salary": 165000, "median_salary": 140000, "currency": "USD", "pay_period": "Yearly"
| # | job_title | location | min_salary | max_salary | median_salary | data_source |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Company Profiles objects from ziprecruiter.com. All fields typed and schema-versioned.
"company_id": "COMP_4829", "name": "TechCorp", "industry": "Information Technology", "company_size": "501-1000", "headquarters": "Austin, TX", "active_jobs_count": 42, "rating": 4.2
| # | company_id | name | website | industry | company_size | headquarters |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Search Results objects from ziprecruiter.com. All fields typed and schema-versioned.
"keyword": "Data Engineer", "location": "Austin, TX", "position": 1, "job_id": "ZR_938472", "title": "Senior Data Engineer", "company": "TechCorp", "is_sponsored": true
| # | keyword | location | position | job_id | title | company |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Location Data objects from ziprecruiter.com. All fields typed and schema-versioned.
"city": "Austin", "state": "TX", "total_jobs": 14291, "avg_salary": 95000, "top_companies": "['TechCorp', 'DataSystems']", "remote_jobs_count": 3102, "last_scraped": "2023-10-24T08:12:00Z"
| # | city | state | zip_code | total_jobs | avg_salary | top_industries |
|---|---|---|---|---|---|---|
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Our ZipRecruiter scraper processes job listings, salary estimates, and company data with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, description, requirements, benefits, and remote status scraped at the job ID level.
Extract ZipRecruiter's proprietary salary estimates, pay ranges, and median compensation data.
Capture company size, industry, headquarters, and active job counts for competitive analysis.
Isolate remote, hybrid, and on-site roles with high precision across all job categories.
Identify promoted jobs and track sponsored placement strategies across different keywords.
Monitor job posting lifecycles, detecting when roles are opened, updated, and closed.
Extract job density and average compensation metrics by city, state, or postal code.
Extract specific technical skills and certifications listed within the raw job descriptions.
Configure continuous pipelines at daily or weekly cadences with change-detection diffing.
Brief in. Clean data out.
Provide job titles, locations, or company names. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and CAPTCHA handling for ziprecruiter.com.
Schema validation, null-rate checks, and salary outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
ZipRecruiter blocks datacentre IPs and paginates heavily. Here is how we build resilient extraction infrastructure.
ZipRecruiter blocks datacentre IPs aggressively. We route requests through residential ISP proxies with realistic browser fingerprints.
Salary graphs and pagination require JavaScript execution. We use Playwright to render SPA elements.
Job descriptions lack uniform formatting. We use fallback chains and regex patterns to normalise unstructured text into distinct fields.
We maintain a hash index of active jobs. Subsequent runs only push new, updated, or closed jobs, reducing compute cost.
Every run emits structured logs. We alert on null-rate spikes and coverage drops. SLA uptime is contractual.
Economic researchers and hedge funds track job posting volume to gauge economic health and sector growth.
HR departments use aggregated compensation data to structure competitive salary bands.
B2B sales teams target companies actively hiring for specific roles, indicating budget and immediate need.
Companies monitor rival hiring velocity, departmental expansion, and new location strategies.
EdTech platforms analyse job requirements to identify emerging software and certification demands.
Niche job boards enrich their own platforms with targeted listings filtered by specific industries or remote status.
"ZipRecruiter holds a massive index of middle-market and enterprise hiring data, but tracking salary trends across thousands of roles requires dedicated infrastructure."
Most teams underestimate the investment required: reliable ZipRecruiter scraping requires residential proxies, JavaScript rendering, CAPTCHA handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our ziprecruiter.com 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 and deduplication. Playwright handles JavaScript rendering and dynamic pagination.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About ziprecruiter.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available job postings is generally permissible under applicable law, focusing strictly on non-authenticated public data.
We use residential ISP proxies, full Playwright browser sessions, and request timing modelled on human behaviour to prevent 403 blocks.
We extract salary ranges when provided by the employer, and ZipRecruiter's proprietary estimates when available.
Pipelines typically run daily to capture new postings and detect closed roles within 24 hours.
Yes. Our change detection system flags when a previously active job URL returns a closed status or 404.
Yes. We configure pipelines to target specific keywords, geographic radii, or company names based on your requirements.
Our smallest packages start at a defined volume of 10,000 jobs per month. Contact us for a scoped quote.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of tech roles or a continuous feed of national salary data — we scope, build, and operate the pipeline. Tell us what you need.
