Managed Snagajob scraping for teams that need hourly pay, shift type and store-level location data. We build the crawler, run it and hand over clean files.
Plans from €169/month · Free project assessment · Reply within 1 business day
You set the scope: named cities, brands, industries, job titles, or a national sweep. We agree the field list, run a sample, and you sign it off before the first full run. After that the job runs on your schedule - daily, weekly or monthly - and lands as CSV, JSON, XLSX, a Google Sheet, or straight into S3, a database or an API endpoint you poll.
Repeat runs are matched on posting id, so you receive new, changed, unchanged and expired rows instead of a fresh flat file every time. Pay revisions and re-postings show up in that diff. We monitor the crawlers and repair them when the site changes its markup, which it does.
Posting pages carry JobPosting structured data, and the pay range sits inside it. On a posting from a February 2025 archived capture, the baseSalary block was a MonetaryAmount whose QuantitativeValue held minValue, maxValue and unitText set to HOUR. That matters: the rate is machine readable and already per hour, so there is no annual figure to divide down.
A standard delivery includes:
We do not collect job seeker profiles. Those sit behind a login and are personal data. Output is limited to posting and employer data, and any personal contact detail an employer typed into a description can be excluded on request.
Keyword search runs at snagajob.com/search/q- followed by the term with plus signs for spaces. Browse pages sit under snagajob.com/find-jobs/ with short facet prefixes: w- for a city and state, i- for an industry, x- for an employment type, plus t- and c- segments that combine with commas. Paging is driven by an opaque continuation token rather than a page number, and the search response reports both the current page size and a separate full match count. Deep paging is a sequence you walk, not a page number you can jump to, and crawl planning has to respect that.
Coverage is assembled from posting pages and browse entry points by city, industry and employment type, and every row is keyed on the numeric posting id.
Postings expire but stay addressable. The record keeps an expiry flag alongside a validThrough date, so a closed listing can still be fetched and dated. That is exactly what makes a historical series possible, and we keep the flag in the output instead of quietly dropping the row.
The site sits behind Cloudflare. Snagajob answers automated clients with a managed challenge page instead of content. We render pages properly and run at a measured rate, and proxy rotation and CAPTCHA handling are part of the service.
Snagajob is a United States job marketplace built for hourly work. It was founded in 2000 and is based in Glen Allen, Virginia. The company traded as Snag from April 2018 and returned to the Snagajob name in November 2019, so both spellings still turn up in older links and coverage. It bought the workforce software firm PeopleMatter in 2016 and sold that business to Fourth Enterprises in 2021. In November 2024 the Boston company JobGet acquired Snagajob, and the site has continued to run under its own name since. We set this out because buyers ask which entity they are looking at. We have no relationship with either company.
The listings are hourly, not salaried, and the employer mix reflects that: restaurants, retail chains, warehousing and distribution, healthcare support, hotels and event staffing. A posting names an employer, a single street address or city, an employment type, and a rate quoted per hour rather than per year. Employment type facets on the site include part time, full time, seasonal part time and gig.
The consequence for anyone collecting Snagajob data is structural rather than cosmetic. National brands post the same role separately for every site they operate, and each copy is a distinct posting with its own identifier, address and rate. One job title at one employer can produce hundreds of near-identical records that differ only by location and pay. Any plan for this source has to decide up front whether those rows are duplicates to be collapsed or the actual signal you came for.
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Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan details →Hourly pay moves locally and it moves quickly. A salaried board tells you roughly what a market pays across a year. Snagajob jobs tell you what one store on one street offered this week. Compensation teams use that to set rates site by site. Staffing firms use it to price bids. Restaurant and retail operators use it to see what the unit down the road is paying before they lose a shift lead to it.
The second reason is the per-location explosion. Because a brand posts the same title across every site it runs, a raw pull is dominated by repeats. Treated properly that is an asset, not noise. Deduplicate on employer plus normalized title plus coordinates and you get a clean count of open roles by metro, which reads directly on where a chain is opening, staffing a season, or quietly stopping.
The third is the operator question. Franchised brands do not set one national wage. The corporate name and the operating entity appear in different fields on the same posting, so a Snagajob scraper that preserves both lets you split a brand's own sites from its franchisees and compare what each pays for the same job title.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed web scraping service. We are not a library and not a self-serve tool. We build the crawler, run it on your schedule, watch it, fix it when the target shifts, and deliver the result in the format your team already works in.
Send us the pages you care about and the columns you need. You get back a sample extract, a scope and a price. If the job is not worth doing, we will say so.
Yes. The ScrapeIt Snagajob API returns posting id, employer, location, hourly pay range, employment type and dates as JSON from an endpoint we host, refreshed on the schedule you set. The same data also comes as CSV or XLSX files or straight into your database.
We keep each posting as its own row, because every store-level listing has its own identifier, address, coordinates and rate. Collapsing them at collection time would destroy the pay variation you are usually buying the data for. We then add the keys that let you roll them up on your side: normalized employer name, normalized job title, and geography. You can report at posting level for pay analysis and at brand plus title level for opening counts, from one extract.
Yes. Pay on this site is quoted per hour. The structured data on a posting carries a minimum, a maximum and a unit of HOUR, and the underlying record also holds a median plus an estimated wage band expressed in percentiles. We deliver employer-stated pay and the site's estimate in separate columns so the two are never confused in your analysis. Tips and bonuses are not a separate numeric field on the site; where an employer mentions them, the text sits inside the job description, and we can flag descriptions that reference tips if that is useful.
Not through a certified franchise flag, because the site does not publish one. What it does publish is the brand name in the company field and the location name as the operator entered it, and those two often differ - a national brand in one field, a named local operator in the other. We deliver both columns plus the full address, which is enough for most teams to classify a site and to compare pay between a brand's own units and its franchisees. We will not label a location as franchised when the source does not say so.
Daily, weekly or monthly, matched to your decision cycle. Hourly hiring is seasonal, so many buyers run weekly through most of the year and daily through the seasonal ramp. Expired postings remain addressable and carry an expiry flag with a validThrough date, so we keep them in the output and mark them rather than deleting the row. That is what lets you measure how long a role stayed open at a given site and how quickly a brand refills it.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Snagajob data from €169/month. Free project assessment, reply within 1 business day.
Scrapeit Sp. z o.o.
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