JustJoin.it Jobs Search Scraper: Automate Polish Tech Job Market Intelligence

Why JustJoin.it Search Data Transforms Recruitment Intelligence

When you think about modern recruitment and market research in the technology sector, having access to broad market data becomes just as important as detailed information about individual positions. This is where the fundamental difference between search-level and detail-level data collection becomes clear. While detailed scrapers focus on extracting every piece of information from individual job postings, search scrapers serve a different but equally critical purpose: they help you understand the market landscape at scale.

JustJoin.it has positioned itself as the go-to platform for technology jobs in Poland, which has emerged as one of Eastern Europe's most vibrant tech hubs. The platform hosts thousands of active job postings at any given time, spanning everything from early-stage startup roles to positions at established international companies. For anyone trying to understand this market, whether you're a recruiter looking for patterns, a company planning expansion, or a researcher tracking industry trends, manually browsing through search results simply doesn't scale.

The search functionality on JustJoin.it allows users to filter by location, technology stack, experience level, and employment type. These filters create distinct market segments that tell different stories about the tech ecosystem. However, accessing this segmented data systematically requires automation. That's precisely what this scraper delivers: the ability to programmatically extract search results across different categories, locations, and filters, giving you a bird's-eye view of the entire job market or specific niches within it.

Understanding What This Scraper Accomplishes

The JustJoin.it Jobs Search Scraper operates at the search results level, which means it processes the listings you see when you perform a search on the platform rather than diving into individual job detail pages. Think of it as capturing the overview rather than the deep dive. This approach serves several strategic purposes that might not be immediately obvious.

When you perform a search on JustJoin.it for, say, AI-related jobs sorted by newest postings, the platform returns a list of opportunities with summary information. This scraper captures that entire list along with the key data points displayed at the search level. The beauty of this approach lies in its efficiency: you can process hundreds of job listings quickly because you're not waiting for detailed pages to load for each position.

The scraper's flexibility in handling multiple URLs simultaneously means you can compare different market segments in a single run. For instance, you might want to compare AI jobs in Warsaw versus Krakow, or see how the number of remote positions compares to on-site opportunities across different technology categories. By feeding the scraper URLs representing these different searches, you gather comparative data that would otherwise require hours of manual clicking and note-taking.

The pagination handling through the offset parameter demonstrates another layer of sophistication. Job search results on JustJoin.it display a limited number of positions per page, typically around twenty. If you want to capture a hundred AI-related positions, you need to move through multiple pages of results. The scraper manages this automatically based on your maximum items configuration, systematically working through pages until it reaches your specified limit or exhausts available results.

This tool particularly benefits recruitment coordinators who need to monitor new job postings daily, market researchers tracking hiring velocity in specific technology sectors, competitive intelligence teams analyzing which companies are hiring for what roles, and data analysts building time-series datasets of job market evolution. Each of these use cases requires breadth rather than depth, making search-level scraping the optimal approach.

Configuring Your Scraping Parameters for Optimal Results

Understanding how to structure your input requires thinking about what you want to achieve with your data collection. The configuration JSON contains several parameters that work together to define the scope and behavior of your scraping operation. Let's walk through each element and understand its purpose in the broader context.

The proxy configuration sits at the foundation of reliable web scraping. When you access JustJoin.it's search functionality, the platform's servers see requests coming from an IP address. If that same IP address makes hundreds of requests in rapid succession, it creates a pattern that looks quite different from normal human browsing behavior. This is where residential proxies become essential. By routing your requests through residential IP addresses, your scraping activity appears distributed across multiple regular users, which aligns with how the platform expects to see traffic. The country selection in proxy configuration can influence response times and sometimes content availability, though for JustJoin.it, which primarily serves the Polish market, the specific proxy country typically matters less than using residential proxies in general.

Here's a thoughtfully configured example that demonstrates these principles:

{
  "proxy": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountry": "US"
  }, // optional
  "offset": 0,
  "max_items_per_url": 20,
  "urls": [
    "https://justjoin.it/job-offers/all-locations/ai?keyword=ai&orderBy=DESC&sortBy=newest"
  ], // Multiple URLs supported
  "ignore_url_failures": true
}

Example Screenshot:

The offset parameter controls where in the search results your scraping begins. Setting it to zero means starting from the very first result, which makes sense for most use cases where you want comprehensive coverage. However, there are scenarios where you might want to start from a different position. For instance, if you're running regular scrapes and your previous run captured the first hundred results, you might set offset to 100 to continue from where you left off, avoiding duplicate data collection.

The maximum items per URL parameter defines your data collection boundary. Setting this to twenty means the scraper will collect up to twenty job listings from each URL you provide. This limitation serves multiple purposes: it helps you control scraping costs, ensures you don't overwhelm the platform with excessive requests, and allows you to sample the market without committing to exhaustive data collection. For exploratory analysis or daily monitoring, smaller numbers like twenty or fifty often suffice. For comprehensive market studies, you might increase this to several hundred, understanding that higher numbers mean longer execution times and higher resource consumption.

The URLs array represents the heart of your scraping strategy. Each URL you include defines a different market segment or search query you want to analyze. These URLs come directly from JustJoin.it's search interface, which means you can use all the filtering and sorting options the platform provides. The example URL demonstrates a search for AI-related positions across all locations, sorted by newest postings in descending order. You might add additional URLs searching for backend developers in Warsaw, frontend roles open to remote work, or senior positions in fintech companies. Each URL represents a question you're asking about the market, and the scraper collects the answers.

The ignore URL failures flag provides resilience in your data collection process. When set to true, the scraper continues processing even if one of your URLs encounters an error. This proves valuable when you're scraping multiple market segments simultaneously: a problem with one URL shouldn't derail your entire operation. The scraper will log which URLs failed while successfully collecting data from the others, allowing you to investigate issues afterward without losing the successful portions of your run.

Decoding the Output Structure and Field Meanings

The data structure returned by this scraper reflects the summary information available at the search results level. Understanding what each field represents and, perhaps more importantly, what it tells you about the market requires looking at both individual field meanings and how they combine to paint larger pictures.

The GUID and Slug fields serve as unique identifiers for each job posting, though they serve slightly different purposes in practice. The GUID represents a globally unique identifier that remains stable even if other aspects of the posting change. Think of it as the job posting's permanent ID number in the system. The slug, derived from the URL structure, provides a human-readable identifier that includes contextual information about the company, position, and location. You'll use these identifiers when tracking specific positions over time, deduplicating results across multiple scraping runs, or creating links between search-level data and detailed information you might scrape separately.

The Title field contains the position name exactly as companies present it, which often reveals important information beyond just the role itself. Job titles in the tech industry have become increasingly nuanced, sometimes indicating specific technology stacks, seniority expectations, or team structures right in the title. For example, "Senior React Developer" tells a different story than "Full Stack Engineer with React Focus," even though both might require similar technical skills. When analyzing title data across hundreds of positions, patterns emerge: you might notice certain companies consistently using "Engineer" versus "Developer," or identify trending titles like "AI/ML Engineer" growing in frequency.

The Required Skills and Nice To Have Skills arrays provide the technical foundation of each position. These fields become particularly powerful when aggregated across multiple job postings. By analyzing which skills appear most frequently in required versus nice-to-have categories, you gain insights into market expectations and emerging technology trends. For instance, if you notice TypeScript moving from nice-to-have to required across many frontend positions over several months, that signals a meaningful shift in market standards. The distinction between required and nice-to-have also helps candidates understand realistic entry points into different roles.

Workplace Type has taken on enormous significance in the post-pandemic job market. This field indicates whether a position is remote, on-site, or hybrid, reflecting one of the most dramatic shifts in work arrangements the tech industry has experienced. When you aggregate this data across different categories or locations, fascinating patterns emerge. You might find that AI roles offer remote work more frequently than embedded systems positions, or that startups in Warsaw are more likely to require on-site presence than established companies. This information influences everything from talent attraction strategies to real estate decisions for companies planning office spaces.

The Working Time field specifies full-time versus part-time arrangements, which matters more in some contexts than others. In Poland's tech market, part-time arrangements are relatively uncommon for permanent positions but appear more frequently in certain niches like consulting or specialized contract work. Tracking this field helps identify segments of the market that accommodate non-traditional work arrangements.

Experience Level segments the market into junior, mid, senior, and expert categories. This field proves invaluable for understanding market structure: a healthy tech ecosystem typically shows balanced distribution across experience levels, while markets with too many senior-only positions might indicate talent development challenges. The experience level also correlates with other factors like salary ranges and required skills, allowing you to build comprehensive pictures of different career stages.

The Employment Types array reflects Poland's unique labor market characteristic: multiple contract types are often available for the same position. This might include traditional employment contracts (umowa o pracę), business-to-business contracts (B2B), or mandate contracts (umowa zlecenie). Each contract type carries different implications for taxation, benefits, and work arrangements. This field helps international companies understand local market practices and enables candidates to compare opportunities on an apples-to-apples basis.

Category ID classifies positions into technical domains like AI, backend development, frontend, DevOps, or data science. These categories provide the primary organizational structure for market analysis. By tracking how many positions fall into each category over time, you can identify which technical specializations are growing or contracting. The category also interacts with other fields: AI positions might show different geographic distribution patterns than mobile development roles, or command different salary ranges.

The Multilocation flag indicates whether a single job posting covers opportunities in multiple cities. This has become increasingly common as companies hire for multiple office locations simultaneously or offer candidates flexibility in choosing their work location. When analyzing hiring patterns, this field helps distinguish between companies expanding to new markets versus companies consolidating hiring efforts.

Location fields including City, Street, Latitude, and Longitude enable geographic analysis of the job market. While the search-level scraper might not always populate street addresses (as these sometimes appear only on detail pages), the city information proves sufficient for most market analyses. Geographic data reveals which cities serve as tech hubs, how job distribution varies across regions, and whether remote work is truly opening opportunities beyond traditional tech centers. The coordinate data, when available, enables sophisticated mapping visualizations that can identify clustering patterns or accessibility to public transportation.

Remote Interview indicates whether companies will conduct their hiring process remotely, which has become a crucial factor for international candidates or anyone unable to travel for interviews. This field often correlates with workplace type: companies offering remote work typically also conduct remote interviews, though not always. Tracking this field helps identify companies with truly remote-friendly cultures versus those simply offering remote work options.

Company Name and Company Logo Thumb URL provide employer identification and branding elements. The company name allows you to aggregate all positions from the same employer, revealing which organizations are hiring most actively. When combined with other fields, you can build comprehensive employer profiles: Does Company X primarily hire for senior roles? What technology stacks do they work with? Are they offering remote opportunities? The logo URL provides visual elements useful when building job boards or presentation materials.

The temporal fields Published At, Last Published At, and Expired At create a timeline for each position. Published At marks when the job first appeared on the platform, while Last Published At indicates the most recent update. The gap between these timestamps tells you whether positions are being actively refreshed or left unchanged. Expired At shows when postings will be removed, helping you understand hiring urgency and timeline expectations. When building time-series datasets, these timestamps enable tracking hiring velocity, seasonal patterns, and how long positions typically remain open.

Open To Hire Ukrainians represents a specific response to geopolitical events and the significant Ukrainian tech talent migration to Poland. Companies explicitly signaling openness to Ukrainian candidates recognize the substantial talent pool this represents while also demonstrating cultural awareness and inclusive hiring practices. This field carries implications for diversity initiatives and market access for international talent.

The Languages array specifies required language proficiencies, which in Poland typically includes some combination of Polish and English, sometimes with other European languages. Language requirements significantly impact candidate pools and can indicate company culture: exclusively English-speaking environments often signal international teams or products, while Polish-required positions might serve the local market.

Apply Method indicates how candidates should submit applications, whether through JustJoin.it's integrated system, external applicant tracking systems, or email. This seemingly small detail reflects company sophistication in hiring processes and can influence application rates.

The promotion-related fields Is Super Offer, Promoted Position, and Promoted Key Filters indicate whether employers have paid for enhanced visibility. These fields matter for several reasons: promoted positions might receive more applications, creating different competitive dynamics. When conducting market research, understanding which positions are promoted versus organic helps you assess genuine market demand versus paid visibility. Companies frequently promoting positions might signal aggressive hiring needs or challenges filling roles.

Here's an example showing how this data structures together:

[
  {
  "guid": "1915d9f5-edd1-4e0d-9061-00aae3349397",
  "slug": "beecommerce-pl-ai-r-d-engineer-vertex-ai-open-source--warszawa-ai",
  "title": "AI R&D Engineer (Vertex AI & Open Source)",
  "required_skills": [
    "AI",
    "n8n",
    "RAG",
    "MCP",
    "Agile",
    "Python",
    "LLM"
  ],
  "nice_to_have_skills": null,
  "workplace_type": "hybrid",
  "working_time": "full_time",
  "experience_level": "senior",
  "employment_types": [
    {
      "from": 16000,
      "to": 24000,
      "currency": "pln",
      "type": "b2b",
      "unit": "month",
      "gross": false,
      "from_chf": 3537.12,
      "from_eur": 3779.632,
      "from_gbp": 3303.264,
      "from_pln": 16000,
      "from_usd": 4403.376,
      "to_chf": 5305.68,
      "to_eur": 5669.448,
      "to_gbp": 4954.896,
      "to_pln": 24000,
      "to_usd": 6605.064
    }
  ],
  "category_id": 25,
  "multilocation": [
    {
      "city": "Warszawa",
      "slug": "beecommerce-pl-ai-r-d-engineer-vertex-ai-open-source--warszawa-ai",
      "street": "Skierniewicka 16",
      "latitude": 52.2312678,
      "longitude": 20.9697266
    }
  ],
  "city": "Warszawa",
  "street": "Skierniewicka 16",
  "latitude": "52.2312678",
  "longitude": "20.9697266",
  "remote_interview": true,
  "company_name": "Beecommerce.pl",
  "company_logo_thumb_url": "https://imgproxy.justjoinit.tech/v9CTUFLCs2jFLstRMDWSG-CeeQOl3IQMHIhGq5Fgb9M/h:200/w:200/plain/https://public.justjoin.it/companies/logos/original/f8bb616b5f1176933342df002f29871c95df8e23.png",
  "published_at": "2025-12-05T15:38:15.318Z",
  "last_published_at": "2025-12-05T15:38:15.318Z",
  "expired_at": "2026-01-04T15:38:15.318Z",
  "open_to_hire_ukrainians": true,
  "languages": [
    {
      "code": "pl",
      "level": "c1"
    },
    {
      "code": "en",
      "level": "c1"
    }
  ],
  "apply_method": "external",
  "is_super_offer": false,
  "promoted_position": null,
  "promoted_key_filters": [],
  "from_url": "https://justjoin.it/job-offers/all-locations/ai?keyword=ai&orderBy=DESC&sortBy=newest#more-filters"
}
]

Step-by-Step Implementation Guide

Beginning your journey with this scraper requires first establishing clear objectives for your data collection. Take time to define what questions you want to answer: Are you tracking hiring trends in specific technologies? Comparing salary ranges across cities? Monitoring which companies are expanding their teams? Your objectives directly shape how you'll configure the scraper and what URLs you'll include.

Once you've defined your goals, visit JustJoin.it and use their search interface to construct the queries that align with your objectives. Apply relevant filters for technology, location, experience level, and workplace type. The platform's interface makes this intuitive: simply search as you normally would, then copy the resulting URL. These URLs contain all the filter parameters encoded in a format the scraper can process. You might create five or ten different search URLs representing different market segments you want to analyze.

With your URLs collected, you'll need an Apify account if you don't already have one. The platform provides free tier access that works well for testing and small-scale scraping operations. Navigate to the JustJoin.it Jobs Search Scraper in the Apify Store and open its configuration interface. You'll see a JSON input field where you'll paste your configuration.

Start by setting up your proxy configuration using residential proxies, which provides the reliability you need for consistent data collection. Configure your offset to zero for a fresh scrape, and set your maximum items per URL based on your needs. For your first run, consider starting with a conservative number like twenty or fifty items to verify everything works correctly before scaling up to larger collections.

Paste your collected URLs into the URLs array, ensuring each is properly formatted as a JSON string within the array structure. Enable the ignore URL failures option to ensure robust operation across multiple URLs. Take a moment to review your configuration for any syntax errors: missing commas, unclosed brackets, or improperly quoted strings are common mistakes that will prevent the scraper from starting.

When your configuration looks correct, initiate the scraper and monitor its progress through the Apify console. The interface shows real-time updates as the scraper processes each URL and collects data. Execution time scales with the number of URLs and items requested: twenty items from three URLs might complete in two or three minutes, while hundreds of items from many URLs could take fifteen to thirty minutes. The console also displays any errors encountered, helping you identify problematic URLs or configuration issues.

Once execution completes, examine the dataset through Apify's preview interface. Verify that the data matches your expectations: Do the job titles make sense? Are the skills arrays populated? Do the timestamps look correct? This quality check catches issues before you invest time in analysis. If something looks wrong, review your input URLs and configuration before running again.

Download your data in the format best suited to your workflow. JSON preserves the full data structure including nested arrays, making it ideal for programmatic processing or database imports. CSV formats work well for quick analysis in Excel or loading into business intelligence tools, though nested fields like skills arrays may require some preprocessing. Excel format combines readability with structure, useful for sharing results with non-technical stakeholders.

For ongoing market monitoring, consider scheduling regular scraping runs through Apify's scheduling interface. Daily or weekly schedules capture market evolution over time without requiring manual intervention. Each run appends to your historical dataset, enabling longitudinal analysis of hiring trends, seasonal patterns, and market shifts.

Strategic Applications and Business Value

The power of search-level data becomes apparent when you move beyond individual job postings to market-level insights. Recruitment agencies can use this data to identify emerging hiring hotspots before they become saturated with competitors. By tracking which companies are posting multiple positions, which technologies are appearing more frequently in job requirements, and which cities are seeing hiring growth, agencies position themselves ahead of market movements rather than reacting to them.

For market researchers and analysts, this scraper provides quantifiable data on questions that otherwise remain speculative. How many AI-related positions have been posted in the last month compared to the previous quarter? What percentage of senior roles offer remote work? Which cities show the fastest growth in tech hiring? These questions transform from educated guesses into data-driven analyses when you have systematic access to search results over time.

Companies planning expansion into the Polish market gain reconnaissance capabilities that inform multiple business decisions. Understanding the competitive landscape for talent helps shape compensation strategies, workplace policies, and recruitment marketing investments. If your analysis reveals that ninety percent of competing AI roles offer remote work, you'll know that limiting your own positions to on-site arrangements puts you at a competitive disadvantage. If Warsaw shows five times as many available positions as Krakow but your analysis reveals that Krakow candidates stay at companies longer, that influences where you establish offices.

Competitive intelligence teams monitor which companies are hiring for what roles, revealing strategic directions before they become public knowledge. A competitor suddenly posting ten backend engineer positions might indicate they're building a new product platform. Multiple AI engineer openings could signal a shift toward machine learning capabilities. This information flows into strategic planning, helping companies anticipate competitive moves and adjust their own strategies accordingly.

Data scientists and analysts can build sophisticated models using search-level data combined with other sources. Time series forecasting predicts hiring volume changes based on historical patterns. Classification models identify which job characteristics correlate with faster time-to-fill or higher application rates. Clustering analysis reveals natural segments within the job market that might not align with traditional category boundaries. The structured nature of this data makes it ideal for quantitative analysis and machine learning applications.

Salary benchmarking, while less precise at the search level than from detailed postings, still provides valuable context when combined with employment types and experience levels. Understanding the relationship between contract types and experience expectations helps normalize compensation comparisons across the market's diverse employment arrangements.

Optimizing Your Data Collection Strategy

Effective use of this scraper extends beyond simply running it occasionally when you remember. Building a systematic approach to data collection multiplies its value while minimizing wasted effort. Start by establishing a clear collection cadence aligned with your decision-making needs. If you're making weekly reports on market trends, weekly scraping makes sense. For strategic quarterly reviews, monthly collection might suffice. The key is consistency: regular intervals enable meaningful trend analysis and pattern recognition.

Structure your URL collection thoughtfully around the market segments that matter to your work. Rather than trying to capture everything, focus on segments where you need visibility. A recruitment agency specializing in AI talent might maintain URLs for AI categories across major Polish cities, various experience levels, and different workplace types. This focused approach provides comprehensive coverage of relevant segments without drowning in irrelevant data.

Implement data validation checks in your processing pipeline to catch anomalies early. Verify that expected fields are populated, that dates fall within reasonable ranges, and that skills arrays contain sensible values. Set up alerts for unusual patterns: sudden drops in result counts might indicate URLs need updating, while spikes could reflect market changes worth investigating. These quality controls ensure you're making decisions based on reliable data.

Consider enriching your search-level data with targeted detail scraping for positions that meet specific criteria. Your search scraper might identify fifty new AI positions this week, but only ten from companies with seed funding or series A funding. Use the detail scraper selectively on those ten to gather comprehensive information without the time and cost of detailed scraping for all fifty. This hybrid approach balances breadth with depth based on your analytical needs.

Document your URL collection strategy and update it as the market evolves. New job categories emerge, companies change their location strategies, and filtering options on the platform may expand. Regularly reviewing and refreshing your URLs ensures you're capturing the current market rather than a historical snapshot that no longer reflects reality.

Analyze your data with appropriate temporal context. Job market dynamics vary significantly by time of year: December hiring slows due to holidays, while September often sees increased activity. When comparing data across months, account for these seasonal patterns rather than interpreting every fluctuation as a meaningful trend. Building sufficient historical data allows you to distinguish seasonal variation from genuine market shifts.

Conclusion

The JustJoin.it Jobs Search Scraper transforms how you understand and interact with Poland's dynamic tech job market. By automating the collection of search-level data, you gain systematic visibility into hiring trends, market structure, and competitive dynamics that would be impossible to capture manually. Whether you're building recruitment intelligence, conducting market research, or informing strategic business decisions, this tool provides the foundation for data-driven approaches to the Polish tech talent market. Begin collecting your market intelligence today and discover insights hidden in the patterns of thousands of job postings.