Programmatic SEO for Job Boards: The Complete Implementation Playbook

Nobody opens Google and types "jobs." They search "remote UX designer jobs," "senior software engineer jobs in Austin," or "entry level marketing coordinator positions." An estimated 70% of job searches begin on Google. Job search is inherently long-tail, and those specific queries represent the vast majority of how people find work online.

The biggest job boards built their organic traffic engines on programmatic pages. Indeed pulls over 360 million monthly visits, powered by millions of landing pages generated from structured job data. LinkedIn Jobs creates location-specific, category-specific, and combination pages at scale. Glassdoor turns company reviews and salary data into thousands of indexed pages. Meanwhile, most independent job boards launch with a homepage, a job feed, and maybe a few hand-written category pages. That leaves enormous search traffic on the table.

Programmatic SEO (pSEO) for job boards is the practice of using structured job data (titles, locations, companies, seniority levels) to automatically generate thousands of search-optimized landing pages from templates. Each page targets a specific long-tail keyword. Instead of manually creating a page for "marketing jobs in New York," you build one template that generates pages for every combination of category and location in your database. The content updates automatically as new jobs match those filters.

This guide is built around how job boards work, not generic pSEO theory adapted from other verticals. The patterns are different. The content freshness requirements are different. The structured data standards are different. What follows covers the specific page architecture, technical implementation, and scaling strategies that work for job boards.

What you'll learn:

1. Why job boards are structurally ideal for programmatic SEO
2. The three content pillars (jobs, companies, candidates) and the page types within each
3. How to build a pSEO engine: templates, JobPosting schema, internal linking, and quality gates
4. Handling expired content without destroying SEO value
5. Competing with Indeed as a niche board
6. Measuring pSEO performance with the right KPIs
7. Preparing for AI search and answer engine optimization

Why job boards are the ideal programmatic SEO use case

Job boards have something most websites don't: structured, multi-dimensional data that updates constantly. Every job posting carries a category, a location, a company, a seniority level, and a set of skills. That data maps directly to the way people search, which means it maps directly to pages Google wants to index.

The NxN pattern is native to job data

Every job posting has multiple structured attributes: category (marketing, engineering, healthcare), location (New York, remote, Austin), seniority level (entry level, senior, manager), company, and skills. Each attribute can be a landing page. Every combination can be a landing page. Cavuno's URL structure demonstrates this:

• Category pages: /jobs/marketing, /jobs/engineering, /jobs/healthcare
• Location pages: /jobs/locations/new-york, /jobs/locations/remote
• Combination pages: /jobs/locations/new-york/marketing, /jobs/locations/austin/software-engineer
• Skill pages: /jobs/skills/react, /jobs/skills/python

The math scales fast. If you have 50 job categories and 100 locations, that's 5,000 combination pages for jobs alone. Add company profiles, company listing pages, salary pages (by role and by company), candidate profiles, and candidate listing pages, and a modest job board can generate tens of thousands of indexed pages, all from the same structured data that powers your core product. Each page targets a real search query with real user intent.

Why long-tail dominates job search

Moz's search demand curve shows approximately 70% of all search traffic comes from long-tail queries (searches with three or more words). Ahrefs' study of their keyword database found that the vast majority of search terms get ten or fewer searches per month — individually tiny, but collectively enormous. In job search, the long-tail concentration is even higher. People don't search broadly. They search specifically because they're looking for a job they can actually apply to: "remote senior software engineer jobs" or "part-time registered nurse jobs in Denver."

Long-tail keywords are also easier to rank for. A niche job board with modest domain authority can realistically rank on page one for "environmental engineer jobs in Portland" while having zero chance at the head term "engineering jobs." Programmatic pages let you capture both the head terms (through category pages) and the long tail (through combination pages) simultaneously.

Product-channel fit and the economics of organic growth

Jeff Chang, a Staff Software Engineer on Pinterest's early Growth Engineering team (where he helped scale the platform to 400M+ MAU), identified four requirements for scaled SEO: growing content, quality content, user-generated text, and strong product-channel fit. Job boards satisfy all four naturally. Growing content happens every time an employer posts. Quality improves as you curate and enrich data. The content is user-generated (by employers). And the product-channel fit is near-perfect: people search Google for jobs, and your job board has jobs.

The economic argument reinforces this. SEO costs don't scale linearly. The upfront investment is in templates, data infrastructure, and crawl budget management. Once that foundation exists, each additional page has near-zero marginal cost. Paid acquisition works in reverse: every incremental click costs the same as the last one, and CPCs for job search keywords are premium. Organic traffic compounds. Paid traffic stops the moment you stop paying.

Cavuno's automated job aggregation creates the structured data foundation for programmatic SEO, no engineering team required. See how it works →

The page types that drive organic traffic

Job boards that win at programmatic SEO don't rely on a single page type. They build a hierarchy of pages across three content pillars: jobs, companies, and candidates. Each pillar has individual detail pages and aggregation pages. The pages work together as a system, with internal links flowing authority between them.

Job pages

Job pages are the core of any job board's programmatic system. They target both sides of search intent: candidates looking for specific roles and broad job seekers browsing by category or location.

Individual job pages are the atomic unit. Every other page type aggregates or contextualizes their data. They target specific, high-intent queries: "Senior Software Engineer at Stripe in San Francisco" or company-and-role combinations. These searches have low volume individually, but the long tail adds up across thousands of listings. This is also where JobPosting schema lives. Google requires structured data on individual job pages only, never on list or aggregation pages (more on this in Section 3).

Category pages target role-based queries like "marketing jobs," "engineering jobs," or "healthcare jobs." URLs follow the pattern /jobs/marketing, /jobs/engineering, /jobs/healthcare. AI-based classification can generate categories automatically from job descriptions, so you don't need to manually tag each listing. Each page shows the current job count, active openings, related searches, and filters for location, salary, and experience level. Indeed and LinkedIn Jobs generate category pages for every major role type, ranking for high-volume head terms and funneling traffic to deeper pages through internal links.

Location pages target geographic queries like "jobs in New York," "remote jobs," or "jobs in Austin." URLs follow the pattern /jobs/locations/new-york, /jobs/locations/remote, /jobs/locations/san-francisco. Location pages are especially valuable for boards focused on a specific region. A job board serving the Austin tech market could dominate "jobs in Austin" and related long-tail variations with a well-optimized location page backed by active listings.

Combination pages merge two dimensions: role and location. "Marketing jobs in New York," "software engineer jobs in Austin," "remote data analyst jobs." URLs follow the pattern /jobs/locations/new-york/marketing or /jobs/locations/austin/software-engineer. The intent is highly specific. A candidate searching "marketing jobs in New York" knows exactly what they want and is more likely to apply. This is where the NxN multiplication creates the most value. Fifty categories across 100 locations can generate 5,000 combination pages. Not all will have active listings at any given time, but the ones that do target high-intent queries with minimal competition.

Skill pages add another dimension: "React jobs," "Python developer jobs," "project management jobs." URLs follow the pattern /jobs/skills/react, /jobs/skills/python, /jobs/skills/project-management. Especially valuable for tech-focused boards.

Role salary pages target compensation research queries: "software engineer salary," "product manager salary San Francisco," or "senior data scientist salary." URLs follow patterns like /salaries/software-engineer or /salaries/software-engineer/san-francisco. Levels.fyi built an entire business on this page type, slicing salary data by role, level, location, and company. These pages capture candidates early in the job search funnel, when they're researching what they should earn before they start applying. They also earn natural backlinks from journalists and career advice sites writing about compensation trends.

Cavuno generates category, location, and combination pages automatically from your aggregated job data, handling templating, threshold logic, and structured data markup without custom development.

Company pages

Company pages capture branded and research-intent queries from both job seekers evaluating employers and recruiters researching the market.

Company profiles target queries like "Stripe jobs," "Salesforce careers," or "Airbnb openings." URLs follow the pattern /companies/stripe, /companies/salesforce. Glassdoor built a significant part of its moat with company pages, layering reviews, salary data, and interview questions on top of job listings. These pages rank for branded searches, earn backlinks from employer branding efforts, and funnel traffic to individual listings. Profiles can include jobs at that company, salary data, benefits, company size, and industry classification.

Company listing pages target market-level queries: "tech companies in Austin," "healthcare companies hiring," or "remote-first companies." URLs follow patterns like /companies/locations/austin for location listings and /companies/markets/healthcare for market listings. Combination pages merge both dimensions: /companies/locations/austin/healthcare targets "healthcare companies in Austin." These aggregation pages capture employers and job seekers researching a market, not a specific company.

Company salary pages target queries like "Stripe software engineer salary," "Google product manager compensation," or "salaries at Salesforce." URLs follow patterns like /companies/stripe/salaries or /companies/google/salaries/product-manager. These complement the role-based salary pages in the Jobs pillar by slicing the same data from the company perspective. Proprietary salary data is a defensible moat that competitors can't easily replicate. The EU Pay Transparency Directive, taking effect in 2026, will accelerate this trend as more employers disclose salary ranges in postings. Boards that aggregate this data first gain a structural advantage. For how salary pages fit into link acquisition strategy, see link building for job boards.

Cavuno's AI company enrichment auto-populates profiles with industry classification, company size, salary data, and structured data. Every company with a listing gets a complete, indexable profile page without manual entry.

Candidate pages

Candidate pages capture the other side of the marketplace: employers searching for talent. The same NxN pattern that works for jobs works for candidates, just oriented toward employers browsing talent.

Individual candidate profiles target name-based and title-based queries: "Jane Smith" or "Head of Product at Canva." These are professional identity searches that LinkedIn currently dominates. URLs follow a pattern like /candidates/jane-smith. Each profile showcases skills, experience, and availability, creating an indexable unit that ranks for the candidate's name and role.

Candidate listing pages target employer talent searches: "React developers available," "marketing managers in Chicago," or "freelance data analysts." URLs follow patterns like /candidates/react-developers, /candidates/locations/chicago, or /candidates/locations/chicago/marketing. Category, location, and combination pages follow the same hierarchy as job pages, giving employers the same filtering and browsing experience that job seekers get on the job side.

Together, candidate pages strengthen your board's two-sided network effects. More indexed profiles attract more employers. More employers post more jobs. More jobs attract more candidates who create profiles.

How the three pillars connect

These page types form a linked hierarchy across all three pillars. Job pages link to the companies posting them. Company pages link to their open jobs and salary data. Candidate profiles link to relevant job categories and locations. The internal linking between pillars is what makes the whole system work, distributing authority across thousands of pages rather than concentrating it in a few.

Building the pSEO engine: from data to indexed pages

Building a pSEO engine involves four steps: (1) design templates with unique data per page, (2) implement JobPosting schema on job detail pages, (3) build internal linking between page types, and (4) set quality gates to prevent thin content from diluting your site's quality.

Template design that Google rewards

Daydream, a pSEO consultancy that has worked with companies like Twingate and Insight Timer on scaled SEO strategies, identifies three eras of programmatic SEO. Era 1 was pure template swapping: change the city name in the H1 and nothing else. Era 2 introduced database-driven differentiation, where each page pulled genuinely unique data from a backend. Era 3 layers AI to generate contextual content at scale while maintaining data accuracy.

Most job boards still operate in Era 1. Swapping "Austin" for "Denver" in the H1 is not enough. Google's algorithms detect pages that differ only by a few token substitutions, and at scale these pages can trigger quality filters that suppress entire sections of a site.

One step toward Era 2 is using vector-based embeddings for related content. Instead of hard-coding "related categories," embeddings surface semantically similar roles: a page for "Ruby jobs" also shows Ruby on Rails, Rails, and Sinatra positions. This makes each page contextually richer without manual curation.

A well-designed landing page includes:

• Live job count for that query, updated dynamically as listings are added or removed
• The job listings themselves, sortable by date, salary, or relevance, filterable by employment type, experience level, or company
• Related searches to surface adjacent pages and keep users navigating your index
• Salary data when available, displayed prominently
• Breadcrumb navigation showing page hierarchy

Cavuno's template system uses dynamic tokens: {{category}}, {{location}}, {{count}}, {{board_name}}. A title template like "{{count}} {{category}} Jobs in {{location}} | {{board_name}}" renders as "47 Marketing Jobs in New York | TechJobs Board" with current listing counts. Customize these in your page SEO settings.

Pages must also perform technically. Slow pages lose rankings regardless of content quality. Core Web Vitals matter. Mobile-first indexing means your mobile experience is the primary signal Google evaluates. Server-side rendering ensures Googlebot sees complete HTML on first request.

JobPosting schema and Google for Jobs

Google for Jobs is not a separate job board. It is an enhanced search result that pulls from sites using structured data. Implementing JobPosting schema correctly makes your listings eligible for this high-visibility slot.

Required properties (per Google's official documentation):

• title — job title only, no codes, addresses, or company names
• description — complete job details in HTML format
• datePosted — ISO 8601 format
• hiringOrganization — Organization type with name and sameAs properties
• jobLocation — Place type with PostalAddress (addressCountry is mandatory)

Recommended properties that improve visibility:

• validThrough — expiration date in ISO 8601 format
• baseSalary — MonetaryAmount with currency and QuantitativeValue (minValue/maxValue, unitText: HOUR, DAY, WEEK, MONTH, or YEAR)
• employmentType — FULL_TIME, PART_TIME, CONTRACTOR, TEMPORARY, INTERN, or OTHER
• directApply — boolean for direct application
• jobLocationType — "TELECOMMUTE" for 100% remote positions
• applicantLocationRequirements — geographic eligibility for remote jobs

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{
  "@context": "https://schema.org/",
  "@type": "JobPosting",
  "title": "Senior Software Engineer",
  "description": "<p>We are looking for a Senior Software Engineer to join our platform team...</p>",
  "datePosted": "2026-01-15",
  "validThrough": "2026-03-15T00:00",
  "employmentType": "FULL_TIME",
  "hiringOrganization": {
    "@type": "Organization",
    "name": "Nexon Technologies",
    "sameAs": "https://www.nexontech.example.com",
    "logo": "https://www.nexontech.example.com/logo.png"
  },
  "jobLocation": {
    "@type": "Place",
    "address": {
      "@type": "PostalAddress",
      "addressLocality": "San Francisco",
      "addressRegion": "CA",
      "postalCode": "94105",
      "addressCountry": "US"
    }
  },
  "baseSalary": {
    "@type": "MonetaryAmount",
    "currency": "USD",
    "value": {
      "@type": "QuantitativeValue",
      "minValue": 180000,
      "maxValue": 240000,
      "unitText": "YEAR"
    }
  }
}

Critical rule: Google requires JobPosting schema on individual job pages only. Never on search results, category, or list pages. Placing schema on aggregation pages violates guidelines and can trigger manual actions.

Google also provides an Indexing API specifically for job postings. Using the Indexing API instead of sitemaps triggers faster recrawling, which matters for time-sensitive content that can fill or expire within days.

Cavuno generates JobPosting schema automatically for every listing. See the full job posting schema guide for property-by-property details.

Internal linking architecture

Ethan Smith, CEO of Graphite and one of the most cited voices on programmatic SEO, puts it bluntly: "90%+ of websites aren't properly using internal links." For job boards generating thousands of pages, internal linking determines whether Google discovers those pages at all.

Smith's crawl points concept refers to pages that distribute link equity and crawl budget to deeper pages. Google allocates a finite crawl budget to your site. With 5,000 or 20,000 programmatic pages, internal links are the primary signal telling it which pages matter and how they relate.

The hub-and-spoke model works well across all three content pillars:

• Job pages: Category hubs (/jobs/engineering) link to combination pages (/jobs/locations/austin/engineering) and top listings. Location pages link to combination pages and recent listings. Individual listings link up to their category, location, and company pages.
• Company pages: Company profiles (/companies/stripe) link to all active listings and salary data for that employer. Company listing pages (/companies/locations/austin) link to individual profiles. Salary pages link to corresponding roles and companies.
• Candidate pages: Candidate listing pages (/candidates/react-developers) link to individual profiles. Individual profiles link to relevant job categories and locations.
• Cross-pillar links: Job listings link to company profiles. Company profiles link to open jobs. Candidate profiles link to relevant job categories. Salary pages connect both job roles and company data.

Breadcrumb navigation is one of the most underused internal linking tools. Every programmatic page should display breadcrumbs showing its position in the hierarchy: Home > Jobs > Engineering > Austin. Breadcrumbs give Google an explicit signal of your site's structure and create clickable upward links from deep pages back to hub pages. Implement BreadcrumbList schema alongside the visible breadcrumbs so Google can display them in search results, further improving click-through rates.

Automated link blocks make this scalable. Related searches surface adjacent categories and locations. "Similar roles" modules link listings with overlapping skills.

Monitor for keyword cannibalization as page count grows. If "Software Engineer jobs in Austin" and "Software Developer jobs in Austin" target identical queries, they compete against each other. Consolidate or differentiate. For more on technical SEO fundamentals, see our job board SEO guide.

The quality gate

Generating pages means nothing if Google does not index them. It's worse if it indexes thin pages that dilute your site's quality signals.

Start with a dynamic XML sitemap that updates as listings are added, filled, or expired. Segment by page type so you can monitor indexation rates separately across all three pillars: job pages, company pages, candidate pages, and their respective sub-types.

Set minimum listing thresholds. Do not index pages with fewer than five to ten active listings. A page for "Data Science jobs in Tulsa" with two listings provides little value to users and signals low quality to Google.

Evaluate content completeness. Pages missing salary data, employer information, or descriptions weaken overall page quality. Monitor content freshness. Pages where all listings are stale should be flagged or noindexed until new postings arrive.

Igal Stolpner, who scaled organic traffic at several large marketplaces, advises: "Prioritize quality over quantity for weeks and months before scaling." Start with 40 to 50 high-quality pages, validate they index and rank, then expand.

Cavuno's content thresholds prevent pages with insufficient listings from being published, automatically managing the quality gate. Monitor indexation by connecting Google Search Console to your dashboard.

Cavuno handles template generation, Google for Jobs schema, and internal linking automatically. Starting at $29/month. Launch your job board in minutes →

The expired content challenge (and how to solve it)

Unlike blog posts or product pages, job listings expire. This makes job board pSEO harder than pSEO in other verticals.

The math compounds quickly. A mid-sized niche board publishing 500 new listings per month with 45-day average lifespans accumulates roughly 500 expired pages every month. After a year, that's 6,000+ dead pages. Dead pages waste crawl budget, create dead-end user experiences, and dilute site quality.

But this is a solvable problem. The operators who solve it gain a durable advantage.

What Google recommends for expired listings

Google's JobPosting documentation is clear on how to handle expired listings:

• Ensure validThrough is populated with a past date
• Remove the page entirely (returning a 404 or 410 status code)
• Remove JobPosting structured data from the page

Failing to expire jobs promptly can trigger manual action penalties. At minimum, expired jobs should be removed from listings, excluded from job counts and pagination, and removed from your sitemap. Read more about expired job handling.

The backfill advantage

If you use job aggregation, expired jobs are automatically replaced with new matching jobs. This creates a self-refreshing content cycle. Category and location pages stay populated even as individual listings churn beneath them.

The key principle: protect aggregate and category pages even when individual listings expire. Category pages accumulate authority over months and years. Individual listings are the leaves. Categories are the branches. Prune dead leaves aggressively to keep the tree healthy.

How niche job boards compete with Indeed (and win)

Every niche board operator has asked the same question: how do you compete with a site that has hundreds of millions of monthly visitors and millions of indexed pages?

You don't compete head-to-head. You compete asymmetrically.

Why depth beats breadth

Google's recent algorithm updates have consistently rewarded depth over breadth, niche expertise over thin aggregation. A niche board with 2,000 deeply enriched listings in a specific vertical can be more competitive than a generalist with millions of shallow listings. A highly relevant, deeply detailed page will often outrank a generic aggregation page, even on a higher-authority domain.

Topical authority is the niche board's advantage

Topical authority makes this concrete: authority is topical and relative. You compete against other results on each specific SERP, not against Indeed across all of job search.

A healthcare job board with salary data, credential requirements, and 200 related pages can outrank Indeed's generic listing page for "travel nurse jobs in San Diego." The Bureau of Labor Statistics projects 1.9 million annual openings in healthcare occupations through 2034. That's a massive, recurring pool of search demand. Google sees a site that consistently demonstrates expertise in healthcare hiring, not just a page that happens to contain the keyword.

The execution sequence matters. Start where your authority is highest: the conversion-intent keywords closest to your niche. A healthcare board should dominate "travel nurse jobs in [city]" before expanding to "healthcare jobs in [city]," and should own that before attempting "jobs in [city]." Each layer builds on the authority established by the one before it.

Topical authority compounds. Branded search volume, relevant backlinks, editorial depth, and community engagement each reinforce the others.

The backlink playbook for niche boards

Topical authority requires backlinks, specifically from relevant sources with relevant anchor text. Three actionable strategies:

Salary and statistical reports. Publish annual niche salary reports. Original data earns links from industry publications, universities, and news outlets. A nursing job board that publishes "2026 Travel Nurse Salary Report by State" will earn backlinks from nursing schools, hospitals, and staffing agencies.

University career center outreach. Nursing school career resources pages for a nursing board. Business school career pages for a fintech board. High-authority .edu backlinks signal relevance. Career centers want curated resources for their students. You want topically relevant backlinks. The exchange is natural.

Industry association partnerships. Professional associations want job boards as member benefits. Backlinks from .org domains, distribution to a pre-qualified audience, and content co-creation opportunities. Especially powerful for association job boards. For the complete playbook on earning backlinks for job boards, see link building for job boards.

The editorial and programmatic flywheel

Bernard Huang, founder of Clearscope and developer of the Ranch-Style SEO framework, argues for disaggregating broad topics into specific, perspective-driven pieces.

Instead of one "Guide to Healthcare Jobs," write "Travel nurse salary by state: 2026 data," "How to transition from bedside nursing to remote health tech," and "What certifications pay best for healthcare technologists." Each piece targets a distinct keyword and links to your programmatic pages.

Editorial content earns backlinks. Backlinks raise domain authority. Higher authority lifts programmatic page rankings. The two content types are symbiotic. For the full playbook on building this editorial engine, see content marketing for job boards.

Ready to give your niche community a job board that competes on Google? Start with Cavuno's Starter plan at $29/month →

Measuring what matters: the pSEO dashboard

Programmatic SEO generates volume. Without focused measurement, you optimize the wrong metrics.

The KPIs that matter

Indexed page ratio is your leading indicator. Monitor trends, not absolute targets. A sudden drop often precedes ranking declines by weeks. If you're generating new pages but indexation is falling, investigate template quality or crawl budget issues before scaling further.

Traffic distribution should show a long-tail pattern: hundreds of pages each with modest traffic, not five pages driving 90% of visits. If traffic is concentrated, your templates or internal linking are not distributing authority effectively.

Zero-traffic pages deserve scrutiny. If a page gets zero clicks after 90 days indexed, evaluate whether to improve, consolidate, or noindex it.

Application rate by page type ties SEO to business outcomes. Which page types across all three pillars actually drive applications? A template generating traffic but zero applications needs different optimization than one with low traffic but high conversion. For deeper analytics guidance, see job board analytics.

The feedback loop

Test small batches of pages targeting a new keyword cluster. Measure for 60 to 90 days. Prune underperformers. Scale winners.

Programmatic SEO rewards patience. Expect 3 to 6 months of groundwork before compound effects take hold.

Track performance in Cavuno's analytics dashboard.

What's next: AI search and the future of job board discovery

The playbook above will drive compounding organic traffic for years. But the discovery landscape is shifting.

Google AI Overviews now appear in a growing percentage of search results. Job-related searches are increasingly triggering these summaries, particularly structured, factual queries like "average salary for data engineers in Austin" or "best cities for nursing jobs."

The good news: the same structural choices that make pSEO work (clean headings, schema markup, concise factual content, tabular data) are precisely what AI systems extract when deciding what to cite. Your programmatic pages with structured salary data, company counts, and clear heading hierarchies are already optimized for machine readability.

E-E-A-T becomes even more critical when AI is the intermediary. AI systems evaluate source authority when deciding what to surface. A job board with genuine first-party data and industry backlinks will be cited over a thin aggregator. Read our E-E-A-T framework for job boards.

Practical steps for AI discoverability

Make every programmatic page machine-readable. Clean header hierarchies, JSON-LD schema, semantic HTML. Could an AI system extract a clear, citable answer from your page in under two seconds? If the answer is buried in paragraph six, restructure.

Add proprietary data that AI systems want to cite. Salary benchmarks, hiring volume trends, credential requirements. Content with unique data is more likely to be cited than generic aggregation.

Build brand authority deliberately. Get quoted in industry publications. Publish original research. Make your job board the source journalists reference when writing about hiring trends in your niche.

These steps also improve traditional Google rankings. There is no trade-off. For the complete SEO playbook, see our job board SEO guide.

The compound advantage starts now

Every combination of role, location, company, and skill is a long-tail keyword waiting to be captured. You don't need a massive engineering team. You need the right page architecture, clean structured data, disciplined internal linking, and patience for compound effects.

Your first 30 days

1. Pick your strongest page type. If you have jobs across many locations, start with location pages. If you cover a deep niche, start with category pages. Launch 40-50 pages.
2. Verify indexation. Submit your sitemap to Google Search Console. Check the coverage report after two weeks. If fewer than half your pages are indexed, investigate template quality.
3. Audit for thin pages. Remove or noindex any page with fewer than five active listings.
4. Check your schema. Run three to five job detail pages through Google's Rich Results Test. Fix errors before scaling.
5. Set up measurement. Track indexed page ratio, zero-traffic pages, and application rate by page type weekly.
6. Expand. Once your first batch indexes and ranks, add combination pages. Then company profiles and salary pages. Then candidate listing pages and skill pages.

Programmatic SEO is the #1 growth lever for job boards, and with Cavuno, you don't need an engineering team to build it. Built by the team behind Himalayas (a remote job board that scaled to millions of users), Cavuno handles automated job aggregation, programmatic page generation, Google for Jobs compliance, and AI-powered search. Start your free trial →