Abstract

This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS)

Documentation

Update frequency: Monthly Dataset source: U.S. Bureau of Labor Statistics Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset. See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/bls-public-data/bureau-of-labor-statistics

At the end of 2024, Alphabet had 183,323 full-time employees. Up until 2015, these figures were reported as Google employees. The alphabet was created through a corporate restructuring of Google in October 2015 and became the parent company of Google as well as several of its former subsidiaries, including Calico, X, CapitalG and Sidewalk Labs. Google’s popularity Google is one of the most famous internet companies in the world, and in May 2024, the most visited multi-platform website in the United States, with over 278 million U.S. unique visitors during that month alone. The California-based multinational internet company has been delivering digital products and services since its creation in 1996. Due to the popularity of its search engine, the verb “to google” has entered the everyday language and the Oxford Dictionary. In addition to that, the company has also crafted itself as one of the most desirable employers, largely due to the many perks it offers in its offices worldwide. Some of the most appealing aspects of working for Google according to its employees include readily available foods and drinks, good working conditions, and ample communal spaces for relaxing, as well as many health benefits and generous salaries. Google offices and employees As of February 2022, Google and Alphabet had more than 70 offices in over 200 cities throughout 50 around the globe, including Germany, Czechia, Finland, Canada, Mexico, Turkey, and New Zealand. The company’s headquarters, also known as “the Googleplex,” are located in Mountain View, California, while other office locations in American states include New York, Georgia, Texas, Washington D.C., and Massachusetts. As Alphabet, the company employs a total over 182 thousand full-time staff, in addition to many other temporary and internship positions. Per the most recent diversity report published in July 2021, most Google employees were male and only 34 percent were female – a figure that has barely changed since the company started reporting on the diversity of its employees in 2016. Furthermore, as of 2021, women occupied only 28.1 percent of leadership positions and 24.6 percent of tech positions. Although Google has regularly stated that the company is committed to promoting ethnic diversity among its personnel, some 54.4 percent of its U.S. employees are White and only 3.3 percent of employees are Black.

Replication data for: "The Impact of Unemployment Insurance on Job Search: Evidence from Google Search Data"

This dataset includes economic statistics on inflation, prices, unemployment, and pay & benefits provided by the Bureau of Labor Statistics (BLS). This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Real-time API access to rich Job Postings Data with 200M+ job postings & Salary Data sourced from Google for Jobs - global aggregate of LinkedIn, Indeed, Glassdoor, ZipRecruiter, and all public job sites across the web.

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

The global economy has been hard hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. A significant number of firms and businesses have gone bankrupt or been scaled down, and many individuals have lost their jobs. The main goal of this study is to support policy- and decision-makers with additional and real-time information about the labor market flow using Twitter data. We leverage the data to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. First, we create a dataset of unemployment-related tweets using certain keywords. Principal Component Regression (PCR) is then applied to nowcast the unemployment rate using the gathered tweets and their sentiment scores. Numerical results indicate that the volume of the tweets has a positive correlation, and the sentiments of the tweets have a negative correlation with the unemployment rate during and before the COVID-19 pandemic. Moreover, the now-casted unemployment rate using PCR has an outstanding evaluation result with a low Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Symmetric MAPE (SMAPE) of 0.921, 0.018, 0.018, respectively and a high R2-score of 0.929.

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.

Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:

• Deduplication Model: Filters out exact and near-duplicates, ensuring unique, high-quality job data.
• Title Taxonomy Model: Categorizes over 20 million titles into 50,000 standardized groups, simplifying analysis.
• Skill & Qualification Models: Extracts hard skills, soft skills, certifications, and degrees, mapped to standard education levels and tailored by job context.
• Job Category Model: Predicts work types (remote, onsite, hybrid), seniority, and employment types (full-time, contract).
• Location Prediction Model: Parses or estimates job locations—ZIP code, city, state—using averaged lat/long values from search-based estimates for accuracy.
• Salary Estimation Model: Predicts minimum, average, and maximum salaries, using parsed and AI-predicted values for a robust salary range.
• Government Classification Models: Assigns SOC codes and titles to postings for structured role insights and regulatory compliance.
• Human Feedback Model: An in-house team of annotators reviews job descriptions and AI outputs, refining model accuracy.

Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.

Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.

Number of persons in the labour force (employment and unemployment), unemployment rate, participation rate and employment rate by province, gender and age group. Data are presented for 12 months earlier, previous month and current month, as well as year-over-year and month-to-month level change and percentage change. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

🧠 data_jobs Dataset

A dataset of real-world data analytics job postings from 2023, collected and processed by Luke Barousse.

  Background

I've been collecting data on data job postings since 2022. I've been using a bot to scrape the data from Google, which come from a variety of sources. You can find the full dataset at my app datanerd.tech.

Serpapi has kindly supported my work by providing me access to their API. Tell them I sent you and get 20% off paid plans.… See the full description on the dataset page: https://huggingface.co/datasets/lukebarousse/data_jobs.

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.

Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:

• Deduplication Model: Filters out exact and near-duplicates, ensuring unique, high-quality job data.
• Title Taxonomy Model: Categorizes over 20 million titles into 50,000 standardized groups, simplifying analysis.
• Skill & Qualification Models: Extracts hard skills, soft skills, certifications, and degrees, mapped to standard education levels and tailored by job context.
• Job Category Model: Predicts work types (remote, onsite, hybrid), seniority, and employment types (full-time, contract).
• Location Prediction Model: Parses or estimates job locations—ZIP code, city, state—using averaged lat/long values from search-based estimates for accuracy.
• Salary Estimation Model: Predicts minimum, average, and maximum salaries, using parsed and AI-predicted values for a robust salary range.
• Government Classification Models: Assigns SOC codes and titles to postings for structured role insights and regulatory compliance.
• Human Feedback Model: An in-house team of annotators reviews job descriptions and AI outputs, refining model accuracy.

Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.

Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.

Diversity in Tech Statistics: In today's tech-driven world, discussions about diversity in the technology sector have gained significant traction. Recent statistics shed light on the disparities and opportunities within this industry. According to data from various sources, including reports from leading tech companies and diversity advocacy groups, the lack of diversity remains a prominent issue. For example, studies reveal that only 25% of computing jobs in the United States are held by women, while Black and Hispanic individuals make up just 9% of the tech workforce combined. Additionally, research indicates that LGBTQ+ individuals are underrepresented in tech, with only 2.3% of tech workers identifying as LGBTQ+. Despite these challenges, there are promising signs of progress. Companies are increasingly recognizing the importance of diversity and inclusion initiatives, with some allocating significant resources to address these issues. For instance, tech giants like Google and Microsoft have committed millions of USD to diversity programs aimed at recruiting and retaining underrepresented talent. As discussions surrounding diversity in tech continue to evolve, understanding the statistical landscape is crucial in fostering meaningful change and creating a more inclusive industry for all. Editor’s Choice In 2021, 7.9% of the US labor force was employed in technology. Women hold only 26.7% of tech employment, while men hold 73.3% of these positions. White Americans hold 62.5% of the positions in the US tech sector. Asian Americans account for 20% of jobs, Latinx Americans 8%, and Black Americans 7%. 83.3% of tech executives in the US are white. Black Americans comprised 14% of the population in 2019 but held only 7% of tech employment. For the same position, at the same business, and with the same experience, women in tech are typically paid 3% less than men. The high-tech sector employs more men (64% against 52%), Asian Americans (14% compared to 5.8%), and white people (68.5% versus 63.5%) compared to other industries. The tech industry is urged to prioritize inclusion when hiring, mentoring, and retaining employees to bridge the digital skills gap. Black professionals only account for 4% of all tech workers despite being 13% of the US workforce. Hispanic professionals hold just 8% of all STEM jobs despite being 17% of the national workforce. Only 22% of workers in tech are ethnic minorities. Gender diversity in tech is low, with just 26% of jobs in computer-related sectors occupied by women. Companies with diverse teams have higher profitability, with those in the top quartile for gender diversity being 25% more likely to have above-average profitability. Every month, the tech industry adds about 9,600 jobs to the U.S. economy. Between May 2009 and May 2015, over 800,000 net STEM jobs were added to the U.S. economy. STEM jobs are expected to grow by another 8.9% between 2015 and 2024. The percentage of black and Hispanic employees at major tech companies is very low, making up just one to three percent of the tech workforce. Tech hiring relies heavily on poaching and incentives, creating an unsustainable ecosystem ripe for disruption. Recruiters have a significant role in disrupting the hiring process to support diversity and inclusion. You May Also Like To Read Outsourcing Statistics Digital Transformation Statistics Internet of Things Statistics Computer Vision Statistics

OverviewThe Jupyter notebook (Bears_Ears_Economic_Impact.pynb) includes all Python code to process data and create all figures reported in the manuscript. The code can also be accessed via Google Colab here (https://colab.research.google.com/drive/19QptKut-FHMs0OIG6N9O7_C9qr_pSZC8?usp=sharing). All code is heavily commented and should be interpretable.Bureau of Labor Statistics Data and AnalysesAll Bureau of Labor Statistics data were acquired from the agency’s Quarterly Census on Employment and Wages online data portal (https://www.bls.gov/cew/downloadable-data-files.htm). These data are provided in the ‘BLS.zip’ file. You can extract these data and place them in a local drive, access the files via the Python code provided, and proceed through the creation of the figures.Economic Impact DataEconomic impact data, provided after the analyses for each scenario was run, are provided in both the ‘Economic_Impact_and_Tax_Revenues_Results.xlsx’ and ‘economic_indicators_data.csv’ files. The former is more interpretable for humans, the latter is called by the Python code provided to create the figures shown in the paper. The latter file will need to be placed in a local drive before executing the Python code which calls it.Comments or QuestionsPlease direct any questions to Dr. Jordan W. Smith (jordan.smith@usu.edu).

This dataset was obtained from the Google Jobs API through serpAPI and contains information about job offers for data scientists in companies based in the United States of America (USA). The data may include details such as job title, company name, location, job description, salary range, and other relevant information. The dataset is likely to be valuable for individuals seeking to understand the job market for data scientists in the USA and for companies looking to recruit data scientists. It may also be useful for researchers who are interested in exploring trends and patterns in the job market for data scientists. The data should be used with caution, as the API source may not cover all job offers in the USA and the information provided by the companies may not always be accurate or up-to-date.

License

CC0

Original Data Source: 2023 Data Scientists Jobs Descriptions

LinkedIn company data with job posting activity insights, location intelligence, and Google Maps enrichment. Our LinkedIn company data helps power company analysis, company valuation, BI, and portfolio management. Updated LinkedIn company data delivered weekly.

OpenWeb Ninja's Job Postings Data API provides business listings, salary data, employer reviews, and additional data, sourced in real-time from Google for Jobs.

The API covers more than 200 million Job Postings Data and Salary Data for any industry/job title and location across the globe. The API serves job postings data points such as job title, location, job description, application links, job expiration, employer info, required experience / education / skills, benefits, responsibilities, and more.

OpenWeb Ninja's Job Postings Data common use cases: - Job Pricing Analysis - Lead Generation & Account-Based Marketing (ABM) - Salary Benchmarking - Job Board Apps, Services, and Sites. - Jobs SEO

OpenWeb Ninja's Job Postings Data Stats & Capabilities: - 200M+ Job Postings worldwide - 249 countries and all industries/job titles covered - 40+ data points per job listing - Aggregates Job Postings Data from all public job sites - Extensive search, querying and filtering capabilities

As of January 2024, the tech startup with the most layoffs was Amazon, with over 27 thousand layoffs, across five separate rounds of layoffs. It was followed by Meta and Google with around 21 thousand and 12 thousand job cuts announced respectively.

Layoffs in in the technology industry

Overall, layoffs across all industries began in 2020 due to the outbreak of the coronavirus (COVID-19) pandemic, with tech layoffs increasing in 2022. In the first quarter of 2023 alone, more than 167 thousand employees had been fired worldwide, a record number of job cuts in a single quarter and more than all of the layoffs announced in 2022 combined, marking a harsh start to of 2023 for the tech sector. From retail to finance and education, all sectors are suffering from this widespread downsizing. However, retail tech startups were hit the most, with almost 29 thousand layoffs announced as of September 2023. Most job losses happened in the United States, where tech giants like Amazon, Meta, and Google are based.

Reasons behind increasing tech layoffs

Layoffs in the technology sector started with the COVID-19 pandemic in 2020 when entire cities were in lockdown and mobility was restricted. Although restrictions loosened up in 2021, events such as the Russia-Ukraine war, the downturn in Chinese production, and rising inflation had a significant impact on the tech industry and continue to represent major concerns for tech companies. As a consequence, companies across the world have yet to overcome all economic challenges, examples of which are rising material and labor costs, as well as decreasing profit margins. To address such difficulties, tech companies have appointed business plans. For instance, in the United States, tech firms planned to focus more on consumer retention, automating software, and cutting operating expenses.

This public dataset was created by the Bureau of Economic Analysis (BEA). It provides a county level view of income, wages, proprietors' income, dividends, interest, rents, and government benefits, including a number of federal and state-level subsidies. Per capita income can be used to gauge the average financial health and associated social needs of an area. Analysis across regions offers a way to assess relative standard of living and quality of life of the population. Trends analysis of these data over time can also uncover specific regions of economic growth or decline across a variety of indicators. These personal income data represent an important lens into the financial security and socioeconomic determinants of health at the community level. They are used by the federal government to allocate hundreds of billions of dollars into state and local programs, to project budgets and trust fund balances, and to develop a more complete picture of labor costs. Personal income statistics can also help illustrate the dynamics between Americans' incomes, spending, and savings. The data summarize per capita income at the county level, including personal income, net earnings, transfer receipts, benefits programs, unemployment insurance, subsidy programs, retirement, dividends, insurance compensation, and several other economic indicators measured by the Department of Commerce or reported to other public agencies. For more information, please refer to the BEA’s Regional Economic Accounts Definitions .

Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126

Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.

Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:

• Deduplication Model: Filters out exact and near-duplicates, ensuring unique, high-quality job data.
• Title Taxonomy Model: Categorizes over 20 million titles into 50,000 standardized groups, simplifying analysis.
• Skill & Qualification Models: Extracts hard skills, soft skills, certifications, and degrees, mapped to standard education levels and tailored by job context.
• Job Category Model: Predicts work types (remote, onsite, hybrid), seniority, and employment types (full-time, contract).
• Location Prediction Model: Parses or estimates job locations—ZIP code, city, state—using averaged lat/long values from search-based estimates for accuracy.
• Salary Estimation Model: Predicts minimum, average, and maximum salaries, using parsed and AI-predicted values for a robust salary range.
• Government Classification Models: Assigns SOC codes and titles to postings for structured role insights and regulatory compliance.
• Human Feedback Model: An in-house team of annotators reviews job descriptions and AI outputs, refining model accuracy.

Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.

Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.

📊 LinkedIn Data for Talent Acquisition, CRM Enrichment & Company Insights LinkedIn data is one of the most essential sources of alternative business intelligence — enabling real-time visibility into company growth, hiring behavior, and lead signals. Canaria’s LinkedIn data product delivers structured insights across LinkedIn company data, LinkedIn job postings, and job market trends, verified and enriched with Google Maps metadata.

This LinkedIn data product is specifically designed for use cases including talent acquisition, business development, CRM data enrichment, HR intelligence, and company analysis. Updated weekly, it empowers organizations to track high-growth companies, hiring signals, employee trends, and location-level expansions — all sourced from LinkedIn and optimized for integration.

🧠 Use Cases: What Problems This LinkedIn Data Solves Our LinkedIn data helps transform fragmented online business profiles into clean, analyzable signals. Whether you need to enrich CRM records, build a talent pipeline, or score leads by job activity, this product enables precision targeting and faster decision-making.

🔍 LinkedIn Company Analysis • Identify company size, industry tags, and employee count trends using LinkedIn company data • Monitor company presence through LinkedIn follower growth, location footprints, and job post activity • Benchmark similar companies using standardized LinkedIn data fields and activity metrics • Track changes in business strategy based on real-time LinkedIn job and company page updates

📈 Talent Acquisition & HR Intelligence • Discover which companies are actively hiring through LinkedIn job postings • Analyze job title demand, skill trends, and role seniority using normalized job market LinkedIn data • Track employer branding and recruiting momentum across key locations • Use LinkedIn data to identify companies hiring for specific departments or regions

⚠️ Risk Detection & Workforce Insights • Spot slowing hiring momentum using LinkedIn job volume trends • Detect early signs of restructuring or regional downsizing • Cross-reference LinkedIn data with Google Maps to validate real-world branch activity • Compare declared headcount with public-facing recruiting behavior

📊 CRM Enrichment & B2B Lead Generation • Enrich company records with verified LinkedIn company data (industry, size, hiring) • Score accounts based on hiring momentum and LinkedIn engagement • Use job postings data to find companies actively hiring for the roles you serve • Identify ideal B2B lead targets based on title trends and LinkedIn hiring signals

🌐 Why This LinkedIn Data Product Is Different 🧠 Matchable Across Systems • Our LinkedIn data is designed to integrate with your job market data, CRM tools, BI platforms, and talent dashboards

📍 Location Intelligence Included • All LinkedIn company profiles include verified HQs and branches, cross-validated with Google Maps metadata

🔁 Weekly Updates • Stay current with weekly-updated LinkedIn data streams, covering new companies, job postings, and hiring shifts

🔗 Taxonomy-Mapped & Clean • Data is normalized into standard LinkedIn company data fields, ready for matching across systems and teams

🎯 Who Benefits from LinkedIn Data • Talent acquisition platforms and recruiting teams • CRM and RevOps teams enriching lead and account records • Strategy and BI teams monitoring workforce and hiring dynamics • Investment teams tracking company growth and hiring behavior • B2B marketers building lead scoring and account targeting models • HR tech tools offering benchmarking and job market insight

📌 Summary Canaria’s LinkedIn Data product delivers enriched, structured, and match-ready business intelligence sourced directly from LinkedIn. By combining LinkedIn company data, LinkedIn job postings, and job market data with location validation via Google Maps, this product enables confident execution across talent acquisition, HR intelligence, CRM enrichment, and company analysis.

Our LinkedIn data is updated weekly, aligned with ATS standards, and compatible with a wide range of platforms and use cases — giving your team an edge in understanding which companies are growing, hiring, or ready to engage.