Data Dictionary: Multi-source Jobs API

Find all data points with explanations available in the Multi-source Jobs Data.

Each category includes a table listing the available data points, their explanations, and data types.

All personal/company information mentioned within this context is entirely fictional and is solely intended for illustrative purposes.

Metadata

Data point
Description
Data type

id

Unified job identifier across all sources

String

job_id_expired

Indicates whether the job cluster is now considered fully expired, with all related jobs deleted and expired. Responses are: 1 – expired. Field value will never change to 0 again; 0 – not expired

Integer

job_sources

List of job postings from different sources for the same job

Array of structs

source_id

Source-specific job ID

String

source

Name of the data source (Professional network, Indeed, Glassdoor, etc.)

String

updated_at

Last update timestamp from this source

Timestamp

url

URL of the job post in the source

String

status

Posting status:

  • Active,

  • Expired,

  • Deleted

String

created_at

Timestamp when the job record was first created

Timestamp

updated_at

Timestamp when the job record was last updated

Timestamp

date_posted

Posting date

Timestamp

valid_through

Expiry date

Timestamp

status

Unified status

  • 1 – active,

  • 2 – expired,

  • 3 – deleted

Integer

Refer to the table example from the data:

Metadata
 "job_id": "12345678",
 "job_id_expired": 1,
 "job_sources": [
    {
        "source_id": "1234567890",
        "source": "professional network",
        "updated_at": "2025-07-01T05:53:10.535",
        "url": "https://www.professional-network.com/jobs/service-specialist-1234567890",
        "status": "deleted"
    },
    {
        "source_id": "indeed_job_1a2b3c4d",
        "source": "indeed",
        "updated_at": "2025-07-10T00:21:00.000",
        "url": "https://www.indeed.com/view-job?jk=1a2b3c4d",
        "status": "inactive"
    },
    {
        "source_id": "glassdoor_job_1020304050",
        "source": "glassdoor",
        "updated_at": "2025-07-22T03:54:10.504",
        "url": "https://www.glassdoor.com/job-listing/service-specialist-?ab=1020304050",
        "status": "inactive"
    }
  ],
 "created_at": "2025-06-02T00:06:00.000",
 "updated_at": "2025-07-22T03:54:10.504",
 "date_posted": null,
 "valid_through": null,
 "status": 3,

Job information

Data point
Description
Data type

title

Standardized job title

String

description

Cleaned full description

String

department

Department name

String

management_level

Management responsibility level

String

is_decision_maker

Marks if the employee is a decision maker, based on active_experience_title

1 – the employee is a decision maker

0 – the employee is not a decision maker

Boolean/integer

seniority

Level of seniority

String

functions

Standardized job functions

Array of strings

external_url

External (company) job URL

String

applicants_count

Number of applicants

String

employment_type

Standardised values to display one of the values: “Full-time “, “Part-time“, “Contract”, “Temporary“, “Internship“, “Volunteer“

String

shift_schedule

List of shift types (e.g., “Monday – Friday”)

Array of strings

accepts_remote

Accepts remote work

Boolean

is_urgently_hiring

Urgent hire

Boolean

is_easy_apply

Supports “quick apply”

Boolean

is_responsive_employer

Employer marked as responsive

Boolean

Refer to the table example from the data:

Job information
 "title": "Backend Engineer",
 "description": "We're looking for a skilled backend engineer to join our growing team.",
 "department": "Engineering and Technical",
 "management_level": "Specialist",
 "is_decision_maker": false,
 "seniority": "Mid-Senior level",
 "functions": [
     "Backend Engineer"
 ],
 "external_url": null,
 "applicants_count": "Be among the first 25 applicants",
 "employment_type": "Full-time",
 "is_urgently_hiring": false,
 "is_easy_apply": false,
 "is_responsive_employer": false,
 "shift_schedule": [
    "Monday to Friday"
 ],
 "accepts_remote": false,

Recruiter information

Data point
Description
Data type

recruiter

Recruiter or contact data, if available

Struct

profile_url

Recruiter’s public profile URL

String

full_name

Full name

String

first_name

First name

String

middle_name

Middle name

String

last_name

Last name

String

Refer to the table example from the data:

Recruiter information
 "recruiter": {
    "profile_url": "https://www.professional-network.com/john-doe",
    "full_name": "John Doe",
    "first_name": "John",
    "middle_name": null,
    "last_name": "Doe"
 },

Location information

Data point
Description
Data type

location

Job location

String

country

Country

String

city

City

String

state

State

String

latitude

Latitude of location (if available)

Float

longitude

Longitude of location (if available)

Float

Refer to the table example from the data:

Location information
"location": "Akron, OH",
"country": "United States",
"city": "Akron",
"state": "Ohio",
"latitude": 41.0812,
"longitude": 81.5188,

Company information

Data point
Description
Data type

company_ids

Source-specific company IDs

Struct

company_id

Professional network company ID

Long

gd_company_id

Glassdoor company ID

String

ind_company_id

Indeed company ID

String

company_name

Standardized company name

String

company_url

Company's website

String

company_domain

Cleaned the main company domain

String

company_logo_url

Logo URL (available from Professional network only)

String

company_description

Short description

String

company_size_range

Company size based on employee count range (as selected by the company profile administrator)

String

company_employees_count

Number of employees on Professional network who associated their experience with the company

Long

company_followers

Company's profile follower count on Professional network

Integer

company_industry

Primary industry

String

company_type

Company type

String

company_enriched_keywords

Categories and keywords that are assigned to the company profile and products across various platforms

Array of strings

company_hq_region

Detailed region where the company's headquarters is located

Array of strings

company_hq_country

Country where the company's headquarters is located

String

company_hq_country_iso2

ISO 2-letter code of the headquarters country

String

company_hq_country_iso3

ISO 3-letter code of the headquarters country

String

company_hq_location

Headquarters location

String

company_hq_full_address

Full address of the headquarters

String

company_hq_city

Headquarters city

String

company_hq_state

Headquarters state

String

company_hq_street

Headquarters street address

String

company_hq_zipcode

Headquarters zip code

String

company_locations_full

List of company locations

Array of structs

location_address

Company location address

String

is_primary

Indicates if this is the primary company location

Integer

Refer to the table example from the data:

Company information
 "company_ids": {
    "company_id": 10779556,
    "gd_company_id": null,
    "ind_company_id": "Example-Company-1"
 },
 "company_name": "Example Company",
 "company_url": "http://www.example-company.com",
 "company_domain": "example-company.com",
 "company_logo_url": "https://www.professional-network.com/logo.png",
 "company_description": "Example Company provides innovative software solutions for businesses.",
 "company_size_range": "501-1000 employees",
 "company_employees_count": 1000,
 "company_followers": 12345,
 "company_industry": "Software",
 "company_type": "Private",
 "company_enriched_keywords": [
    "AI", 
    "Cloud", 
    "SaaS", 
    "Business Tools"
  ],
 "company_hq_region": [
    "Americas", 
    "Northern America", 
    "AMER"
 ],
 "company_hq_country": "United States",
 "company_hq_country_iso2": "US",
 "company_hq_country_iso3": "USA",
 "company_hq_location": "Austin, TX, United States",
 "company_hq_full_address": "123 Main Street; Suite 500; Austin, TX 78701, US",
 "company_hq_city": "Austin",
 "company_hq_state": "Texas",
 "company_hq_street": "123 Main Street; Suite 500",
 "company_hq_zipcode": "78701",
 "company_locations_full": [
    {
      "location_address": "123 Main St, Cityville, USA",
      "is_primary": true
    }
 ],

Company funding

Data point
Description
Data type

company_last_funding_round_name

Last funding round (e.g., Series A)

String

company_last_funding_round_announced_date

Date of the latest funding round

Date

company_last_funding_round_lead_investors

Lead investors

Array of strings

company_last_funding_round_amount_raised

Amount raised in the most recent round

Long

company_last_funding_round_amount_raised_currency

Currency of the raised amount

String

company_last_funding_round_num_investors

Number of investors

Long

Refer to the table example from the data:

Company funding
 "company_last_funding_round_name": "Example funding round name",
 "company_last_funding_round_announced_date": "2025-08-20",
 "company_last_funding_round_lead_investors": [
   "John Doe"
 ],
 "company_last_funding_round_amount_raised": 10000000,
 "company_last_funding_round_amount_raised_currency": "USD",
 "company_last_funding_round_num_investors": 1,

Company technologies

Data point
Description
Data type

company_technologies_used

Technologies used by the company

Array of structs

technology

Technology name

String

first_verified_at

When was technology first verified

Date

last_verified_at

When was technology last verified

Date

Refer to the table example from the data:

Company technologies
 "company_technologies": [
    {
        "technology": "figma",
        "first_verified_at": "2023-06-01",
        "last_verified_at": "2025-07-09"
    },
    {
        "technology": "microsoft",
        "first_verified_at": "2025-06-20",
        "last_verified_at": "2025-08-04"
    }
 ]

Salary information

Data point
Description
Data type

salary

All salary records for this job

Array of structs

min_value

Minimum advertised salary

Float

max_value

Maximum advertised salary

Float

currency

Salary currency

String

type

One of the following values: "Year", "Month", "Week", "Day", "Hour"

String

text

Raw/cleaned salary text

String

source

Source name

String

Refer to the table example from the data:

Salary information
"salary": [
    {
        "min_value": 28.33,
        "max_value": 43.11,
        "currency": "USD",
        "type": "HOUR",
        "text": "$28.33 - $43.11 an hour",
        "source": "indeed"
    }
 ]

Benefits

Data point
Description
Data type

benefits

List of job benefits ("healthcare", "remote", etc.)

Array of strings

Refer to the table example from the data:

Benefits
 "benefits": [
    "employee discount",
    "life insurance",
    "paid sick time",
    "vision insurance",
    "dental insurance",
    "health savings account",
    "health insurance"
 ]

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