Job Data Glossary: Understand the Technical Language

API Rate Limiting: API rate limiting is a mechanism that restricts the number of requests a client can make to an API within a defined time window. For job posting APIs, rate limits are typically tied to subscription tiers - free plans allow fewer requests per minute than paid plans. Understanding rate limits is essential when building data pipelines that consume job posting data at scale, as exceeding them can interrupt ingestion workflows.See also: Job Posting API, Pricing and plan limits
Applicant Tracking System (ATS): An Applicant Tracking System (ATS) is software used by employers to manage job applications, publish open positions, and track candidates through the hiring process. Major ATS platforms - including Workday, Greenhouse, Lever, iCIMS, Taleo, SmartRecruiters, Personio, and Teamtailor - host millions of live job postings on their career page infrastructure. Because ATS career pages are employer-direct sources, data collected from them is considered high quality and free of republication duplicates.See also: ATS sources covered, Greenhouse API alternative, Workday API alternative
ATS Career Page: An ATS career page is a job listing page hosted directly on an employer's website, powered by an Applicant Tracking System platform. Unlike job boards where employers post for distribution, ATS career pages are the authoritative source of a company's open roles. Scraping ATS career pages produces employer-direct job data - marked with the isDirect flag in normalised datasets - which is valuable for building accurate hiring signals and avoiding duplicate listings.See also: Data schema and fields, ATS platforms covered
ATS Scraping: ATS scraping is the automated collection of job postings directly from employer career pages hosted on Applicant Tracking System platforms such as Workday, Greenhouse, or Lever. Unlike scraping job boards, ATS scraping captures postings at their original source, producing high-quality, duplicate-free data that reflects actual hiring activity. This technique is central to building direct-source job datasets used in labour market research, sales intelligence, and talent analytics.See also: 56 ATS platforms covered, Greenhouse data, Lever data
Bulk Data Feed: A bulk data feed is a large batch export of structured records delivered on a recurring schedule, typically daily or hourly. Job posting bulk feeds contain all new or updated postings for a given country or date range, packaged as compressed files in formats such as NDJSON or Parquet. Bulk feeds are suited for job boards, data warehouses, and analytics platforms that need to ingest large volumes of job data without making individual API calls for each record.See also: Job Data Feeds, File format and schema
Business Intelligence (Job Data): Business intelligence using job data involves applying job posting analytics to strategic business decisions - identifying industry trends, tracking competitor expansion, benchmarking hiring activity, and spotting market opportunities. By analysing patterns across millions of job postings, organisations can validate market sizing assumptions, track fast-growing sectors, and monitor workforce shifts in near real-time. Job posting data complements traditional business intelligence sources with a high-frequency signal directly tied to operational investment decisions.See also: Business Intelligence use case, Market Intelligence use case
Career Page: A career page is a section of a company's website dedicated to listing open job positions and communicating employer branding. Career pages are either built in-house or powered by an Applicant Tracking System (ATS) platform. In the job data industry, career pages represent the highest-quality source because postings come directly from the employer with no intermediary republication, meaning data is fresher and less likely to contain duplicates.See also: Sources and portals, Data schema
Company Hiring Signal: A company hiring signal is an observable pattern in a company's job posting activity that indicates strategic intent - such as entering a new market, adopting a new technology, or preparing for a product launch. Hiring signals derived from job postings often surface 3-6 months before public announcements, giving sales, investment, and competitive intelligence teams an early advantage. Common signals include a spike in open roles for a specific location, a new job category, or a technology keyword appearing in job descriptions.See also: Sales Intelligence use case, Company Hiring Signal API, Competitive Intelligence use case
Competitive Intelligence (Job Data): Competitive intelligence using job data involves monitoring competitors' hiring activity to infer their strategic direction, product roadmap, geographic expansion, and headcount changes. Because companies must post job ads before they can hire, job posting patterns reveal intent months before earnings calls, press releases, or regulatory filings. Analysis might include tracking when a competitor starts hiring engineers in a new location, opens a new country office, or ramps headcount in a specific product area.See also: Competitive Intelligence use case, Investment Intelligence use case
Data Portability: Data portability in the context of job data refers to the right to store, export, analyse, and redistribute job posting data acquired through a subscription or purchase. Unlike display-only API restrictions - common with job board publisher APIs - data portability licences allow consumers to index data, use it in machine learning models, and share derived insights without restriction. Full data portability is a key differentiator when evaluating job data providers for analytics and AI training use cases.See also: Indeed API alternative, Reed API alternative, Terms of Service
Data Schema: A data schema defines the structure, field names, and data types of a dataset. In job posting data, the schema determines which fields are collected and normalised across all sources - such as jobTitle, companyName, companyDomain, salary, skills, workplaceType, and location. A consistent, well-documented schema is essential for building reliable analytics dashboards, machine learning models, and downstream integrations on top of multi-source job data.See also: Full schema reference, API field documentation
Employment Office: An employment office is a government-operated job portal that publishes open positions, typically from public sector employers and registered private companies. Examples include EURES (EU), USAJobs (US federal), Gov.uk Find a Job (UK), Arbeitsagentur (Germany), and France Travail (France). Job posting datasets that include employment office data provide official, verified listings that supplement commercial job board coverage, and are particularly valuable for labour market research and policy analysis.See also: Employment offices covered, Labour Market Intelligence
GDPR Compliance: The General Data Protection Regulation (GDPR) is an EU regulation governing the collection, storage, and processing of personal data. For job data providers operating in Europe, GDPR compliance means collecting only publicly accessible job posting data (not personal candidate data) and storing it on EU infrastructure under appropriate data processing agreements. Choosing an EU-based, GDPR-compliant job data provider reduces regulatory risk for European organisations subject to GDPR.See also: About Techmap (EU-based), Privacy Policy
Hiring Signal: A hiring signal is an actionable piece of intelligence derived from a company's job posting activity. Examples include a sudden increase in engineering roles (suggesting a product build-out), postings for a new country market (suggesting geographic expansion), or a technology keyword appearing for the first time in job descriptions (suggesting budget allocation). Hiring signals from job posting data are widely used in B2B sales prospecting, competitive analysis, and investment due diligence.See also: Sales Intelligence use case, Company Hiring Signal API
Hiring Velocity: Hiring velocity measures the rate at which a company or industry is adding job postings over time. High hiring velocity indicates organisational growth, market expansion, or accelerated investment; declining velocity can signal cost-cutting or contraction. Investors and analysts use hiring velocity as a leading indicator for company performance, while recruiters use it to time outreach and assess competitive compensation dynamics within a given labour market.See also: Investment Intelligence use case, Competitive Intelligence use case
Historical Archive (Job Data): A historical archive of job postings is a dataset containing job ads collected and preserved over an extended period - typically years - rather than only current active listings. Historical archives enable longitudinal analysis of hiring trends, skills demand cycles, salary evolution, and industry growth over time. Techmap's historical archive covers all job postings collected since January 2020, comprising over 405M records across 250 countries and territories.See also: Historical Job Datasets, Trend Identification use case
Investment Intelligence: Investment intelligence through job data involves using hiring patterns as alternative data signals for investment decisions. Analysts track portfolio companies' hiring velocity, new hiring geographies, and technology adoption signals to form forward-looking views on growth before financial results are published. Job postings are considered high-quality alternative data because they reflect real operational activity - companies must advertise roles before they can fill them.See also: Investment Intelligence use case, Business Intelligence use case
Direct Source: Direct sources are platforms where job postings are listed by the employer without any job board in between - typically the company's own careers website, powered by an ATS platform such as Workday, Greenhouse, or Lever. Because there is no intermediary, the posting is the original: it goes live as soon as the employer publishes it, contains the full unmodified text, and disappears as soon as the role is filled. This contrasts with indirect sources such as job boards and aggregators, where employers submit ads for distribution and the platform controls timing, formatting, and visibility. Direct sources are considered the highest-quality input for job data collection because they reflect the employer's actual hiring activity without republication lag or content loss.See also: Full schema and field definitions, Source portal types
Job Aggregator: A job aggregator is a website or service that collects and indexes job postings from multiple sources - including job boards, company career pages, and employment offices - presenting them in a unified search interface. Unlike job boards, aggregators typically do not accept direct employer postings but instead crawl the web for existing listings. In a job data provider's source mix, aggregator-sourced postings supplement direct and board-sourced data to improve geographic and niche industry coverage.See also: Job aggregators covered, Adzuna API alternative
Job Board Backfilling: Job board backfilling, also known as job posting syndication, involves automatically adding job postings from external sources - such as partner job boards, company websites, and professional networks - to a central job board. This process enhances the job board's content by providing a broader array of listings, making it more comprehensive and dynamic. Job board backfilling saves time for both operators and employers, ensuring a more extensive and up-to-date job search experience for users.See also: Job Board Backfill use case, Job Data Feeds for backfill
Job Board Hosting: Job board hosting, often termed job board platform services, is the practice of outsourcing the technical management of an online job board to third-party providers. This approach allows job board owners to focus on content curation, monetisation, and user experience without requiring extensive technical infrastructure. Benefits of using a job board hosting provider include cost savings, rapid setup, and ongoing technical support, providing a scalable solution compared to self-hosting the entire job board stack.See also: Job Board Backfill use case
Job Boards: Job boards, often referred to as job portals or career platforms, serve as virtual marketplaces where companies post job openings and job seekers can apply. Widely used job boards include LinkedIn, StepStone, Reed, SEEK, Naukri, and Dice, each serving specific regional or professional markets. For data consumers, job boards are one of three primary source types - alongside ATS career pages and job aggregators - and provide broad coverage of employer demand across industries and geographies.See also: Job Board Backfill use case, Job boards covered, Indeed API alternative
Job Crawler: A job crawler is a program that starts from a seed URL, follows every link it encounters, and indexes any page it recognises as a job posting. When the crawler lands on a page it evaluates whether the content matches a job posting - based on structured data markup, URL patterns, or page structure - and if so extracts fields such as job title, description, company, location, and posting date. Crawlers then continue following outbound links from that page, recursively discovering new job postings across job boards, employer career pages, and employment portals without needing a pre-defined list of URLs.See also: Managed job data feeds, Bright Data alternative, Zyte alternative
Job Data: Job data, often interchangeably used with employment data, constitutes a wide-ranging collection of information crucial for businesses, job seekers, and researchers. This data goes beyond individual job postings, offering comprehensive insights into labour market statistics, trends, and industry-specific employment figures. With details on job growth, vacancy rates, and skills demand, job data serves as a valuable resource for informed decisions across hiring, training, investment, policy development, and competitive intelligence.See also: Data schema and coverage, Job Posting API
Job Datafeeds: Job datafeeds, also known as job data feeds or job feed services, are automated streams of real-time job information gathered from diverse sources including job boards, company websites, and employment portals. These feeds provide seamless updates on job listings, ensuring timely and accurate information for downstream systems. Data teams can use job datafeeds to continuously analyse labour market trends, identify sales signals, monitor emerging job titles and skills, and inform strategic workforce planning.See also: Job Data Feeds product
Job Datasets: Job datasets, also referred to as job posting datasets or employment datasets, consist of structured collections of job postings encompassing details such as job titles, responsibilities, qualifications, and hiring organisations across whole countries or date ranges. By using job datasets, data scientists can uncover meaningful patterns, identify correlations, and contribute to informed decision-making for lead generation, talent acquisition, workforce planning, and strategic business initiatives.See also: Job Datasets product
Job Posting API: A job posting API is an application programming interface that provides programmatic access to structured job posting data. Developers use job posting APIs to query and filter live job postings by parameters such as country, source, keyword, date range, employment type, or company. Compared to bulk data feeds, APIs are suited for real-time queries, low-volume use cases, and interactive applications. Several major job boards have discontinued or restricted their own APIs, driving adoption of commercial third-party job posting API providers with self-serve access.See also: Job Posting API, API documentation, Indeed API alternative
Job Posting Normalization: Job posting normalization is the process of transforming raw job listing data from diverse sources into a consistent, standardised format. Because each job board and career page structures its data differently, normalisation maps fields such as job title, location, employment type, and salary into a common schema with consistent field names and value formats. Normalised job data is significantly easier to analyse, compare across sources, and integrate into analytics tools than unprocessed HTML or varied JSON formats.See also: Normalised schema reference, Pre-normalised datasets
Job Postings: Job postings, also known as job ads or job listings, serve as detailed announcements crafted by employers to highlight available positions within their organisations. These announcements encompass key details including job titles, responsibilities, qualifications, salary ranges, and application processes. Data analysts rely on job postings to dissect labour market trends, discern industry changes, and extract actionable insights, empowering strategic decision-making and effective workforce planning.See also: Job Posting API, Data sources
JSON-LD: JSON-LD is a lightweight format for representing structured data on the web. It is a subset of JSON specifically designed for embedding structured data in web pages in a way that is understandable by search engines and AI systems. The benefits of using JSON-LD for structured data include improved search engine visibility, enhanced understanding of webpage content, and the facilitation of rich snippets that produce more informative search results. Job postings, organisations, FAQs, and breadcrumbs are common schema types expressed in JSON-LD.See also: Job data schema
Labour Market Data: Labour market data encompasses all quantitative information about employment conditions, including job vacancy counts, hiring rates, unemployment statistics, skills demand, salary levels, and workforce mobility patterns. Job posting data is a primary source of real-time labour market data, providing more timely insights than official government statistics, which are typically published with a multi-month lag. Researchers, policymakers, and HR analytics teams use labour market data to understand economic conditions and plan workforce strategies.See also: Labour Market Intelligence use case, Historical datasets
Labour Market Intelligence: Labour market intelligence is the systematic analysis of job posting data to understand hiring trends, skills demand, salary dynamics, and workforce supply and demand. By monitoring job postings at scale across industries, geographies, and time periods, organisations can identify emerging skills shortages, benchmark compensation packages, and forecast talent demand. Labour market intelligence is used by governments, universities, staffing firms, HR software vendors, and economic research institutions to inform strategy and policy.See also: Labour Market Intelligence use case, Historical datasets for LMI, Trend Identification use case
Market Intelligence (Job Data): Market intelligence using job data involves analysing hiring patterns to understand industry dynamics, competitive landscapes, and economic conditions at a macro level. By examining job posting volumes, required skill sets, and geographic hiring patterns across industries, analysts can identify fast-growing sectors, emerging technology categories, and shifting geographic hiring concentrations. This approach to market intelligence is often faster than traditional research because job postings reflect current investment decisions in near real-time.See also: Market Intelligence use case, Competitive Intelligence use case
NDJSON (JSON Lines): NDJSON (Newline Delimited JSON), also known as JSON Lines or JSONL, is a file format where each line contains a valid, self-contained JSON object. It is widely used for bulk data pipelines and file exports because files can be read line by line without loading the entire dataset into memory, making it suitable for streaming and large-scale ingestion. Techmap delivers job posting bulk data in NDJSON format - one JSON object per posting per line - packaged as gzip-compressed files organised by country and collection date.See also: File format reference, Developer documentation
Parquet: Apache Parquet is an open-source columnar storage file format optimised for analytical workloads. Unlike row-based formats such as CSV or JSON, Parquet stores data by column, enabling highly efficient compression and fast query execution on analytical queries that only touch a subset of fields. Job posting datasets delivered in Parquet format integrate natively with cloud data warehouses such as Snowflake, BigQuery, and AWS Redshift, and analytical frameworks like Apache Spark, DuckDB, and Pandas.See also: Parquet dataset downloads, Available formats
Partner API: A partner API is an API that requires formal approval or a commercial partnership agreement before access is granted, unlike public self-serve APIs available to anyone with an account. Several major job boards - including LinkedIn and StepStone - only make their job data APIs available to vetted partners meeting specific criteria. Partner API requirements create significant barriers for data and analytics teams, often driving adoption of third-party job data providers with self-serve access and transparent pricing.See also: LinkedIn Jobs API alternative, StepStone API alternative
Publisher API: A publisher API is an API model where job posting data is provided for display purposes only - the consumer may show job listings in their interface but cannot store, analyse, or redistribute the data. Indeed operated a widely used Publisher API under this model until discontinuing it in 2023. Publisher APIs with display-only restrictions are a poor fit for analytics, machine learning, and data warehousing use cases that require full data portability.See also: Indeed API alternative, Reed API alternative
Real-time Job Data: Real-time job data refers to job posting records that are collected and made available with minimal delay after publication, typically within hours of a job being posted. Unlike historical datasets that capture past postings, real-time job data reflects the current state of hiring activity across markets. Real-time access is essential for use cases such as job board backfilling, sales prospecting alerts, and competitive hiring monitoring where timeliness directly determines the value of the data.See also: Real-time Job Posting API, Hourly data feeds, Sales Intelligence use case
Recruitment Intelligence: Recruitment intelligence uses job posting data to improve hiring decisions and HR strategy. By analysing competitors' job descriptions, required qualifications, salary ranges, and benefits packages, talent acquisition teams can benchmark their own postings, identify skills gaps in candidate pools, and optimise job ad language to attract stronger applicants. Recruitment intelligence tools typically ingest normalised job posting datasets to provide benchmarking and competitive comparison capabilities across industries and geographies.See also: Recruitment Intelligence use case, Job Datasets for HR analytics
Sales Intelligence (Job Data): Sales intelligence from job data involves identifying prospective B2B customers by analysing what companies are currently hiring for. When a company posts jobs for a specific technology, department, or seniority level, it signals budget allocation and buying intent. Sales teams use job posting data to identify timing triggers - for example, a wave of cybersecurity postings suggests the company is about to invest in security tooling - enabling more timely and contextually relevant outreach to target accounts.See also: Sales Intelligence use case, Company Hiring Signal API, Job data provider comparison
Salary Data: Salary data in job postings refers to compensation information stated in a job ad - typically as a range, exact figure, or hourly rate, along with currency and pay period. Not all job postings include salary information, but where present, it is valuable for compensation benchmarking, pay equity analysis, and skills valuation. In normalised job posting datasets, salary is stored as a structured object containing the amount, currency, period, and min/max range fields, enabling cross-source comparison.See also: Salary fields in schema, Labour Market Intelligence, Recruitment Intelligence
Skills Extraction: Skills extraction is the automated identification and tagging of required skills and technologies from job posting description text. Using natural language processing (NLP) techniques, skills extraction maps free-text job descriptions onto a structured list of skills, including programming languages, tools, frameworks, certifications, and soft skills. Extracted skills data is used in labour market intelligence to track skills demand trends, identify emerging technologies, and analyse the evolving requirements for specific job roles.See also: Skills field in schema, Labour Market Intelligence, Trend Identification
Skills Gap Analysis: A skills gap analysis identifies the mismatch between the skills employers are currently seeking and those available in the workforce. Job posting data is the primary input for skills gap analysis because it directly reflects real, current employer demand at scale. By analysing the frequency and distribution of required skills across job postings in a given market or sector, researchers, training organisations, and policymakers can identify where workforce development investment is most urgently needed.See also: Labour Market Intelligence, Historical datasets
Structured Data: Structured data refers to information organised in a predefined, machine-readable format - typically rows and columns, JSON objects, or key-value pairs with consistent field names. Raw job postings collected from the web are unstructured HTML text; structured job data is the result of parsing, extracting, and normalising that content into consistent fields such as jobTitle, companyName, location, and salary. Structured job data can be directly ingested into databases, data warehouses, and analytical tools without additional preprocessing.See also: Structured schema reference, Pre-structured datasets
Trend Identification: Trend identification in job data involves analysing longitudinal patterns in job postings to detect shifts in employer demand, emerging skills, and evolving job roles. By comparing job posting volumes for specific titles, technologies, or industries over time, analysts can identify which skills are declining, which are emerging, and where geographic hiring concentration is shifting. Job data is uniquely suited for trend identification because it reflects employer intent at high frequency, often months ahead of mainstream commentary.See also: Trend Identification use case, Labour Market Intelligence
Vacancy Rate: Vacancy rate is the proportion of positions in a given market, industry, or company that are currently unfilled and being actively recruited for. Job posting data is widely used as a proxy for vacancy rates because open positions must be advertised to be filled. Vacancy rate analysis using job posting data provides more granular and timely insights than official government vacancy surveys, which are typically aggregated at a high level and published with a significant delay.See also: Labour Market Intelligence, Historical datasets for vacancy analysis
Web Crawling: Web crawling is the automated process of browsing publicly accessible websites to collect and extract information, performed by software programs called crawlers or spiders. In the context of job data, web crawling collects postings from company career pages, job boards, and employment portals. Building a production-grade web crawling infrastructure for job data requires managing bot detection countermeasures, pagination logic, structural page changes across hundreds of sites, proxy management, and large-scale deduplication.See also: Managed job data feeds, Bright Data alternative, Oxylabs alternative