The wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

This dataset provides comprehensive real-time job listing data aggregated from multiple job boards and company websites. It includes detailed job information such as titles, descriptions, requirements, salaries, locations, and company details. The data is continuously updated to provide the most current job opportunities. Users can leverage this dataset for job search applications, market research, salary analysis, and career development tools. Whether you're building a job search platform, conducting employment market analysis, or developing career guidance tools, this dataset provides current and reliable job market data. The dataset is delivered in a JSON format via REST API.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. By leveraging advanced web scraping technology, this dataset delivers access to job market trends, salary insights, and in-demand skills. A valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence, this data helps businesses stay ahead in a dynamic job market.

Key Features:

✅ 214M+ Job Postings Tracked – Data sourced from 92 company websites worldwide. ✅ 7M+ Active Job Openings – Continuously updated to reflect real hiring demand. ✅ Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅ Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅ Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅ Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes in the Dataset:

General Information: - id (UUID) – Unique identifier for the job posting. - type (constant: "job_opening") – Object type. - title (string) – Job title. - description (string) – Full job description extracted from the job listing. - url (URL) – Direct link to the job posting. - first_seen_at (ISO 8601 date-time) – When the job was first detected. - last_seen_at (ISO 8601 date-time) – When the job was last observed. - last_processed_at (ISO 8601 date-time) – When the job data was last updated.

Job Metadata:

• contract_types (array of strings) – Employment type (full-time, part-time, contract).
• categories (array of strings) – Job industry categories (engineering, marketing, finance).
• seniority (string) – Seniority level (manager, non_manager).
• status (string) – Job status (open, closed).
• language (string) – Language of the job posting.

Location Data:

• location (string) – Full location details from the job description.
• location_data (array of objects) – Structured location details: -- city (string, nullable) – City where the job is located. -- state (string, nullable) – State or region. -- zip_code (string, nullable) – Postal/ZIP code. -- country (string, nullable) – Country. -- region (string, nullable) – Broader geographical region. -- continent (string, nullable) – Continent name. -- fuzzy_match (boolean) – Indicates if the location was inferred.

Salary Data:

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Salary currency (USD, EUR, GBP).
• salary_low_usd (float, nullable) – Minimum salary converted to USD.
• salary_high_usd (float, nullable) – Maximum salary converted to USD.
• salary_time_unit (string, nullable) – Time unit (year, month, hour).

Occupational Data (ONET):

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (Computer and Mathematical).
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (Python, JavaScript, AI).

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Job Openings Docs https://docs.predictleads.com/v3/guide/job_openings_dataset

Explore the "CareerBuilder US Jobs Dataset – August 2021," a valuable resource for understanding the dynamics of the American job market.

This dataset features detailed job listings from CareerBuilder, one of the largest employment websites in the United States, and provides a comprehensive snapshot of job postings as of August 2021.

Key Features:

• Extensive Job Listings: Includes thousands of job postings across various industries and sectors, providing a broad view of employment opportunities in the US.
• Detailed Information: Each listing contains essential details such as job titles, company names, locations, job descriptions, employment types (full-time, part-time, contract), required qualifications, and salary ranges.
• Insights into Trends: Analyze trends in employment, including the most in-demand skills, top hiring companies, popular job roles, and geographic distribution of job opportunities.
• Ideal for Research: This dataset is perfect for researchers, HR professionals, and data analysts interested in understanding the current state of the job market, developing recruitment strategies, or studying labor market dynamics.

By leveraging this dataset, you can gain valuable insights into the US job market as of August 2021, helping you stay ahead of industry trends and make informed decisions. Whether you're a job seeker, employer, or researcher, the CareerBuilder US Jobs Dataset offers a wealth of information to explore.

About Employment Service

Employment Service (ES) is one component of the suite of services known as Employment Ontario (EO). ES provides Ontarians with access to all the employment services and supports they need in one location, so they can find and keep a job, apply for training, and plan a career that’s right for them. The goal of the ES program is to help Ontarians find sustainable employment.

Employment Service is delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services provided by ES are tailored to meet the individual needs of each client and can be provided one-on-one or in a group format.

Employment Service has two broad categories: unassisted and assisted services.

Unassisted services, or the Resource and Information (RI) service component, provides individuals with information on local training and employment opportunities, community service supports, and resources to support independent or “unassisted” job search. These services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. The RI component also helps employers to attract and recruit employees and skilled labour by posting positions and offering opportunities to participate in job fairs and other community events.

This service component is available to all Ontarians as there are no eligibility or access requirements.

Assisted services are offered to individuals who display the need for more intensive, structured, and/or one-on-one employment supports, and includes the following components:

·
job search assistance (including individualized assistance in career goal setting, skills assessment, and interview preparation)

·
job matching, placement and incentives (which match client skills and interested with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and

·
job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)

The service provider will develop with the assisted services client an ES service plan – and will monitor, evaluate, and adjust this plan over the duration of the service plan.

To be eligible for assisted services, clients must be unemployed (defined as working less than twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.

About ES Service Provider Funding

Service providers that deliver Employment Service sign agreements with MLTSD that cover individual fiscal years (defined as April 1st to March 31st). These agreements specify at which service delivery site(s) the service provider agrees to provide ES, the performance expectations for each service delivery site (SDS), and the funding that MLTSD will provide to the service provider to deliver ES at each SDS.

Funding for ES is provided through two budget categories: operating funds and flow-through funds, with the latter further divided between Employment and Training Incentives for Employers and Employment and Training Supports for Clients/Participants. These three budget lines cover the normal costs of delivering all aspects of ES for both unassisted and assisted clients; for exception one-off expenditures, such as relocation, service providers can apply for Field Supports, which is the fourth and final budget line. Please see below for additional details on each of these four budget lines:

1. Operating Funds are for the direct delivery of all of the components of ES (unassisted and assisted). Costs related to the provision of the ES that would be considered part of a service delivery site’s day-to-day operations include, but are not limited to:

·
staff and management salaries;

·
hiring and training of staff (including professional development);

·
marketing (signage, paper/web ads, outreach, etc.);

·
facilities (rent);

·
facilities (mortgage payments) ONLY the interest portion of a mortgage payment is allowed as an Operating cost;

·
other direct operating expenditures related to the delivery of the Employment Service.

Service delivery sites are able to attribute no more than 15% of their operating funds for administrative overhead. Administrative overhead recognizes costs necessary for operating an organization but not directly associated with the delivery of the Employment Service. For example, a portion of the salaries/benefits of the Executive Director, IT, and/or financial staff who work for the entire organization but may spend a portion of their time dedicated to administrative functions that support ES. Note that Operating Funds cannot be used for termination and severance costs.

1. Employment and Training Incentives for Employers are funds for employers to provide employment and on-the-job training opportunities in ES (up to $8,000 per person. The $8,000 is made up of a maximum of $6,000 for training incentives and an additional $2,000 for the Apprenticeship Employer Signing Bonus, if applicable).

2. Employment and Training Supports for Clients/Participants are funds for Clients/Participants in assisted components (up to $500 per Client/Participant). These supports are determined based on family income and are intended, on a temporary basis, to help Clients/Participants address any financial barriers to participation in ES. Client eligibility for these supports is determined on the basis of need and the Low-Income Cut-offs (LICO) income value for the locality. Supports can cover costs such as:

·
transportation;

·
work clothing or clothing/grooming needed to achieve credibility;

·
special equipment, supplies and equipment;

·
certification charges (that may be applied to some short term courses);

·
short term training costs such as books, materials;

·
emergency or infrequent child care;

·
language skills assessment/academic credential assessment;

·
translation of academic documents (for internationally trained individuals);

·
workplace accommodation needs for persons with disabilities.

1. Field Supports are funds that may be provided through a formal in-year request to support ES Recipients with one-time exceptional expenditures not normally included as part of ongoing operations. Requests will be reviewed on a case by case basis and approved at the sole discretion of the Ministry. Purchases related to Field Support cannot be made without prior written approval from the Ministry.

Service providers have discretion over the use of their funds within the following parameters:

·
Operating funds are allocated against an identified level of service;

·
In situations of co-location of ES with other programs and services, ES funds must only be used to cover costs directly related to the delivery of ES;

·
Operating funds cannot be used for major capital expenditures, such as the purchase or construction of facilities. Purchase of equipment and furniture directly related to the effective delivery of the contracted program is allowable;

·
A service provider must obtain prior written approval from the Ministry to shift funds between service delivery sites or communities;

·
A service provider must not transfer funds between the four budget lines given above unless it obtains the prior written consent of the Ministry; and

·
A service provider should not anticipate additional funds, although the Recipient should discuss any issues with the Ministry.

A funding model is used to determine funding levels for the Operating Funds budget line. This model is based on the target number of assisted services clients that each service delivery site agrees to serve in that fiscal year. Note that no targeted funds are provided to deliver unassisted services; these are to be funded out of the allocation provided to service delivery sites on the basis of their target number of assisted services clients.

The ES funding model allocates resources in five ranges based on the target amount of assisted services client the service delivery site is to achieve. For each range there is a sliding scale of possible funding amounts per assisted services client, and service delivery sites with higher assisted service client targets generally receive lower per client funding, on the basis that larger service delivery sites are able to achieve economies of scale. Also note that because of this graduated approach to ES funding it is possible that a service delivery site that has its assisted services client target increase may actually receive less overall funding if the target increase shifts it from one range to the next.

The five funding ranges are:

A/S Client Target

Funding Range per A/S Client

Up to 399

$1,000 to $2,950

400 to 899

$925 to $2,100

900 to 1,499

$850 to $1,200

1,500 to 1,999

$795 to $1,000

2,000 and Above

$795

The actual funding amount per assisted services client within each range is determined by reference to two groups of indicators: Location and Labour Market Environment. A service delivery site is assessed against each indicator, and within each group the number of indicators that are assessed as valid/true is totaled. The value, along with the assisted services client target, is then compared to a table to determine the funding value for Location

Employment (workplace) by industry from the Business register and employment survey (BRES). This data excludes self-employed but includes proprietors Employment = employees + working proprietors. Working Proprietors are sole traders, sole proprietors, partners and directors. This does not apply to registered charities. Numbers have all been rounded to the nearest 100 Before the BRES first existed in 2009, the ABI collected employment data by industry. The two surveys are not directly comparable. The BRES is a business survey which collects both employment and financial information. Only employment information for the location of an employees workplace is available from Nomis The BRES is based on a sample of approximately 80,000 businesses and is used to provide an estimate of the number of employees. The difference between the estimate and its true value is known as the sampling error. The actual sampling error for any estimate is unknown but we can estimate, from the sample, a typical error, known as the standard error. This provides a means of assessing the precision of the estimate; the lower the standard error, the more confident we can be the estimate is close to the true value. NOMIS website article

This dataset excludes farm based agriculture data contained in SIC class 0100.

Data and charts accompanying the 'Business Register Employment Survey 2010: London' publication

The ABI was replaced by the Business Register and Employment Survey (BRES) from 2009 onwards, therefore this dataset will no longer be updated.

More on ONS website

https://data.london.gov.uk/dataset/workplace-employment-industry-borough

License: UK Open Government Licence

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

PredictLeads Job Openings Data provides real-time hiring insights sourced directly from company websites, ensuring the highest level of accuracy and freshness. Unlike job boards that rely on aggregated listings, our dataset delivers unmatched granularity on job postings, salary trends, and workforce demand - making it a powerful tool for HR, talent acquisition, and market analysis.

Use Cases: ✅ Job Boards Enhancement – Improve job listings with, high-quality postings. ✅ HR Consulting – Analyze hiring trends to guide workforce planning strategies. ✅ Employment Analytics – Track job market shifts, salary benchmarks, and demand for skills. ✅ HR Operations – Optimize recruitment pipelines with direct employer-sourced data. ✅ Competitive Intelligence – Monitor hiring activities of competitors for strategic insights.

Key API Attributes:

• id (string, UUID) – Unique job posting identifier.
• title (string) – Job title as posted by the employer.
• description (string) – Full job description.
• url (string, URL) – Direct link to the job posting.
• first_seen_at (ISO 8601 date-time) – When the job was first detected.
• last_seen_at (ISO 8601 date-time) – When the job was last detected.
• contract_types (array of strings) – Employment type (e.g., full-time, contract).
• categories (array of strings) – Job categories (e.g., engineering, sales).
• seniority (string) – Job seniority level (e.g., manager, entry-level).
• salary_data (object) – Salary range, currency, and converted USD values.
• location_data (object) – City, country, and region details.
• tags (array of strings) – Extracted skills and keywords from job descriptions.

PredictLeads Docs: https://docs.predictleads.com/v3/guide/job_openings_dataset

This data is subject to change at any time due to personnel actions that are consistent with the City's approved Pay Plan. Employee compensation is limited to the following pay: regular wages, over time, productivity enhancement, shift differential, standby, incentive pay, allowances, and leave payouts. The data is a snapshot as of December 31st of the reported year.

Job descriptions, pay ranges, and benefits information is available at: https://www.phoenix.gov/hr/job-descriptions

Total compensation information is available at: https://www.phoenix.gov/hr/current-jobs/total-compensation-information

Employee terms by unit is available at: https://www.phoenix.gov/hr/employee-terms-by-unit

The Department of Jobs and Small Business publishes quarterly reports on a range of job seeker compliance data. Quarterly compliance data has been available on the department's website since 2006. The data relates to those job seekers on activity tested income support payments for the relevant quarter, and many of the indicators are broken down by categories such as age, gender, payment type, indigeneity and employment service programme - i.e. jobactive, Disability Employment Services (DES) and the Community Development Programme (CDP). The data includes a range of statistics on job seeker attendance at appointments with employment services providers, income support payment suspensions, the number and type of non-compliance reported and the number of participation failures and financial penalties applied.

The Arlington Profile combines countywide data sources and provides a comprehensive outlook of the most current data on population, housing, employment, development, transportation, and community services. These datasets are used to obtain an understanding of community, plan future services/needs, guide policy decisions, and secure grant funding. A PDF Version of the Arlington Profile can be accessed on the Arlington County website.

Job Search Engines Market size was valued at USD 19.14 Billion in 2024 and is projected to reach USD 105.4 Billion by 2031, growing at a CAGR of 14.2% during the forecast period 2024-2031.

Rising Employment Opportunities: As economies around the world expand, businesses scale up operations, subsequently creating more job opportunities. This growth in employment facilitates a greater need for efficient job search engines to match job seekers with potential employers. Certain sectors such as technology, healthcare, and renewable energy are growing rapidly, leading to an increase in job vacancies. Specialized job search engines cater to these niches, driving market growth. Populous countries with large, young workforces contribute to the increased number of job seekers utilizing job search engines. Growing Internet Penetration: As internet access becomes more widespread globally, more individuals can use online platforms, including job search engines. This is particularly notable in developing regions where internet adoption is accelerating. Lower costs of internet services and devices have made it more feasible for a broader audience to go online, boosting the user base for job search engines. The availability of high-speed internet makes the use of job search engines more convenient and effective, supporting features such as real-time notifications and the ability to upload and download large files (e.g., resumes and portfolios). Shift to Digital Recruitment: The integration of data analytics and AI in recruitment processes enables job search engines to offer more personalized and streamlined experiences for both job seekers and employers. Improved algorithms and machine learning facilitate better job-candidate matching, increasing the effectiveness and appeal of digital recruitment platforms. Digital platforms reduce the costs associated with traditional recruitment methods (e.g., print advertising and in-person job fairs). Employers benefit from decreased hiring costs, while job search engines profit from increased business. Increased Mobile Device Usage: With the global proliferation of smartphones, a significant portion of job searches is conducted via mobile apps and mobile-optimized websites. Job search engines that offer robust mobile platforms are experiencing higher engagement. Mobile devices provide unparalleled flexibility and convenience, allowing users to search for jobs, set up alerts, and apply for positions from anywhere, at any time. Innovative mobile apps designed by job search engines offer features such as GPS-based job searches, voice and video interviews, and chat support, which enhance the user experience. Technological Advancements: Innovations in AI, machine learning, and big data analytics enhance the functionality of job search engines, providing personalized job recommendations and improving match accuracy.

Remote Work Trends: The rise of remote work opportunities, especially post-pandemic, has increased the demand for job search engines that specialize in remote and freelance job listings.

Employer Branding: Companies use job search engines to build and promote their employer brand, attracting top talent by showcasing their work culture, benefits, and career opportunities.

Government Initiatives: Supportive government policies and initiatives aimed at reducing unemployment and promoting job creation boost the usage of job search engines.

Gig Economy Growth: The expanding gig economy, characterized by short-term contracts and freelance work, drives the need for specialized job search engines catering to gig workers.

Globalization and Cross-Border Employment: Increasing globalization and the trend of cross-border employment necessitate job search engines that facilitate international job searches and candidate sourcing.

The historical Prevailing Wage public disclosure data available on the Office of Foreign Labor Certification (OFLC) web page in the Performance Data section. This dataset includes data collected from Prevailing Wage applications during previous fiscal years. It includes information on employers, geography, job details, etc. for participants in the Prevailing Wage program. Historical Prevailing Wage public disclosure data is available on the OFLC website in the Performance Data section. Data is available as Excel files in aggregate form for previous fiscal years at https://www.dol.gov/agencies/eta/foreign-labor/performance.

Job Bank receives and displays job postings in two main ways. The first method is through employers who create an account and advertise jobs directly on Job Bank's website. Job postings advertised directly on Job Bank include information such as the job title, codes from the National Occupational Classification (NOC) and the North American Industry Classification System (NAICS), work location, number of vacancies, salary and benefits, hours of work, job requirements, and employment terms. The second method is through external contributors, who are private and provincial job boards which Job Bank has agreements with. Job Bank displays job postings meeting specific eligibility criteria that are shared by external contributors through XML feeds. Job postings received from external contributors include information such as the job title, code from the NOC, work location, salary, hours of work, and employment terms. A cell containing NA indicates that the information was not available.

Work from home (WFH) has been a part of the professional landscape for over two decades, yet it was the COVID-19 pandemic that has substantially increased its prevalence. The impact of WFH on careers is rather ambiguous, and a question remains open about how this effect is manifested in the current times considering the recent extensive and widespread use of WFH during the pandemic. To answer these questions, this article investigates whether managerial preferences for promotion, salary increase and training allowance depend on employee engagement in WFH. We take into account the employee’s gender, parental status as well as the frequency of WFH. Furthermore, we examine whether managers’ experience with WFH and its prevalence in the team moderate the effect of WFH on careers. An online survey experiment was run on a sample of over 1,000 managers from the United Kingdom. The experiment was conducted between July and December 2022. The findings indicate that employees who WFH are less likely to be considered for promotion, salary increase and training than on-site workers. The pay and promotion penalties for WFH are particularly true for men (both fathers and non-fathers) and childless women, but not mothers. We also find that employees operating in teams with a higher prevalence of WFH do not experience negative career effects when working from home. Additionally, the more WFH experience the manager has, the lesser the career penalty for engaging in this mode of working. Our study not only provides evidence on WFH and career outcomes in the post-pandemic context but also furthers previous understanding of how WFH impacts careers by showing its effect across different groups of employees, highlighting the importance of familiarisation and social acceptance of flexible working arrangements in their impact on career outcomes.

The WageIndicator Survey is a continuous, multilingual, multi-country web-survey, counducted across 65 countries since 2000. The web-survey generates cross sectional and longitudinal data which might provide data especially about wages, benefits, working hours, working conditions and industrial relations.The survey has detailed questions about earnings, benefits, working conditions, employment contracts and training, as well as questions about education, occupation, industry and household characteristics.Research Focus:The WageIndicator Survey is a multilingual questionnaire and aims to collect information on wages and working conditions. As labour markets and wage setting processes vary across countries, country specific translations have been favoured over literal translations. The WageIndicator Survey includes regularly extra survey questions for project targeting specific countries, for specific groups or about specific events.These projects usually address a specific audience (employees of a company, employees in an industry, readers of a magazine, members of a trade union or an occupational association, and alike). The data of the project questions are included in the dataset.Sample:The target population of the WageIndicator is the labour force, that is, individuals in paid employment as well as job seekers. In addition to workers in formal dependent employment the survey aims to include apprentices, employers, own-account workers, freelancers, workers in family businesses, workers in the informal sector, unemployed workers, job seekers individuals who never had a job, as well as retired workers and housewifes school pupils or students with a job on the side and persons performing voluntary work.The WageIndicator data is derived from a volunteer survey, inviting webvisitors to the national WageIndicator websites to complete the web-survey. Annually, the websites receive millions of web-visitors.Bias:Non-Probability web based surveys are problematic because not every individual has the same probability of being selected into the survey. The probability of being selected depends on national or regional internet access rates and on numbers of visitors accessing the webiste. Data of such surveys form a convenience rather than a probability sample. Due to the non-probability based nature of the survey and its selectivity the obtained results cannot be generalized for the population of interest; i.e. the labor force.Comparisons with representative studies found an underrepresentation of male labour force, part-timers, older age groups, and low educated persons.Besides other strategies to reduce the bias the WageIndicators provides different weighting schemes in order to correct for selection bias.Data Characteristics:The data is organised in annual releases. The data of the period 2000-2005 is released as one dataset. Each data release consists of a dataset with continuous variables and one with project variables. The continuous variables can be merged across years. All variable and value labels are in English. The data does not include the text variables and verbatims form open-ended survey questions, these are available in Excel-Format upon request.Spatial Coverage:The survey started in 2000 in the Netherlands. Since 2004, websites have been launched in many European countries, in North and South America and in countries in Asia. From 2008 on web sites have been launched in more African countries, as well as in Indonesia and in a number of post-Soviet countries.For each country each, the questions have been translated. Multilingual countries employ multilingual questionnaires. Country-specific translations and locally accepted terminology have been favored over literal translations.Rights: Due to the confidential character of the WageIndicator microdata, direct access to the data is only provided by means of research contracts. Access is in principle restricted to universities and research institutes.

Employment and Vacancies - Table 215-17001 : Number of construction sites, manual workers engaged, vacancies and job opportunities at public and private sector sites analysed by type of site

Through its Employment and Financial Services (EFS) division, Assisted Living and Social Services (ALSS) programs form a strong foundation of support to help many Albertans find and keep jobs. The ministry provides financial support, employment services, career resources, referrals, information on job fairs and workshops, and local labor market information. The goal is to help individuals and families gain independence by providing opportunities to enhance their skills to get jobs. The alis.alberta.ca website provides employment resources to help Albertans enhance their employability, plan for education and training, make informed career choices, and connect to and be successful in the labour market. This dataset provides information on web traffic statistics for the alis website, including information on pageviews and web sessions, demographic information for web sessions, and traffic information for the alis YouTube channel at: https://www.youtube.com/user/ALISwebsite.