Employee Data

Clean Employee Data

Dictionary: Clean Employee Data

38min

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Clean Employee Data provides high-quality, structured workforce data that is ready for immediate use. Our data is meticulously cleaned and enriched, enabling businesses to streamline operations, enhance decision-making, and optimize workforce analysis.
By leveraging Clean Employee Data, organizations can reduce engineering overhead, gain access to additional insights, and work with optimized data formats for improved efficiency. The data is available in JSONL, Parquet, and CSV formats, ensuring faster downloads and seamless integration.
With flexible retrieval options—including flat file downloads and API access—businesses in sales tech, HR intelligence, and investment sectors can efficiently access the workforce insights they need.
Clean Employee Data is derived from our Base Employee Data﻿. 
Overview
The data points are separated into collections to visualize the data better. 
Data collections
﻿Metadata﻿ 
﻿Identifiers﻿ 
﻿Skills﻿ 
﻿Experience﻿ 
﻿Education﻿ 
﻿Hidden collections﻿ 
﻿Location﻿ 
﻿Recommendations and connections﻿ 
﻿Languages﻿ 
﻿Certifications﻿ 
﻿Courses﻿ 
﻿Awards﻿ 
﻿Activity﻿ 
﻿
Metadata
Data point
Processing
Description
Data type 
member_last_updated
Cleaned
Date the record was last updated
String
member_is_deleted
Raw
Indicates whether the profile was accessible:
1 - deleted or private
0 -  publicly available
Integer
Meta data
"member_last_updated": "2023-07-29",
"member_is_deleted": 0
"member_last_updated": "2023-07-29",
"member_is_deleted": 0
﻿
Data point
Cleaning action
member_last_updated
Value is converted to the yyyy-mm-dd format.
﻿
Identifiers
Data point
Processing
Description
Data type
member_id
Raw
Identification key in our database
Integer
member_websites_professional_network
Raw
Professional network profile URL
String
member_picture_url
Raw
Profile picture URL
String
member_full_name 
Cleaned
Full name
String
member_name_first
Raw
First name
String
member_name_middle
Enriched
Middle name
String
member_name_last
Enriched
Last name
String
member_shorthand_names
Raw
A list of all historical employee shorthand names
Array of strings
member_follower_count
Raw
Number of profile followers
Integer
Identifiers
"member_id": 4290,
"member_full_name": "John Leonardo Doe",
"member_name_first": "John",
"member_name_middle": "Leonardo",
"member_name_last": "Doe",
"member_websites_professional_network": "https://www.professional_network.com/in/john-leonardo-doe",
"member_picture_url": "https://static.lnk.com/aero-v1/sc/h/9c8pery4andzj6ohjkjp54ma2",
"member_shorthand_names": [
        "john-lenoardo-doe"
    ],
"member_follower_count": 445,
"member_id": 4290,
"member_full_name": "John Leonardo Doe",
"member_name_first": "John",
"member_name_middle": "Leonardo",
"member_name_last": "Doe",
"member_websites_professional_network": "https://www.professional_network.com/in/john-leonardo-doe",
"member_picture_url": "https://static.lnk.com/aero-v1/sc/h/9c8pery4andzj6ohjkjp54ma2",
"member_shorthand_names": [
        "john-lenoardo-doe"
    ],
"member_follower_count": 445,
﻿
Data point
Cleaning action
member_full_name
Special characters/emojis are removed; 
Any words that follow a comma or in parentheses are removed;
Titles (preceding or following the name) are removed.
member_name_middle
Parsed from member_full_name.
member_name_last
Parsed from member_full_name.
﻿
Skills
Data point
Processing
Description
Data type
member_skills
Enriched
List of employees' skills
Array of strings
Skills
"member_skills": [
        "creative",
        "design",
        "electronics",
        "photography",
        "programming"
    ]
"member_skills": [
        "creative",
        "design",
        "electronics",
        "photography",
        "programming"
    ]
﻿
Data point
Enriching action
member_skills
Enriched with our ML model from different description fields. 
﻿
Experience
Data point
Processing
Description
Data type 
member_description
Raw
Job position description
String
company_id
Enriched
Identification key for the company associated with the employee's experience
Integer
member_job_title
Cleaned
Current job position title
String
is_decision_maker
Enriched
Indicates whether the employee is a decision-maker based on member_job_title
1 - Employee is marked as a decision-maker in the current role 
0 - Employee is not marked as a decision-maker in the current role
Integer
member_job_description
Raw
Current job position description
String
member_headline

Raw
Job title found in the profile headline 
String
member_generated_headline
Raw
A user-written headline that can be found in web search, also viewed and other publicly available spaces.
It serves the same purpose as the title but is derived from a different source, potentially providing more accurate and up-to-date profile information. 
This field should be used in place title as it reflects the latest user activity.
String
total_experience_duration
Enriched
Summed up experience (displayed as years and months)
String
total_experience_duration_months
Enriched
Summed up employee experience (displayed as months)
Integer
Experience
"member_description": "Results-driven professional with extensive experience in supervisory roles, business analysis, project management, and financial analysis. Skilled in managing enterprise-wide implementations of healthcare information systems, with expertise in gathering and defining client data requirements.",
"company_id": 1111111,
"member_job_title": "Senior Consultant",
"is_decision_maker": 1,
"member_job_description": "Senior Business analyst @ Company123",
"member_headline": "Healthcare Consultant",
"member_generated_headline": "Healthcare Consultant at Company 123",
"total_experience_duration": "2 years 4 months",
"total_experience_duration_months": 28,
"member_description": "Results-driven professional with extensive experience in supervisory roles, business analysis, project management, and financial analysis. Skilled in managing enterprise-wide implementations of healthcare information systems, with expertise in gathering and defining client data requirements.",
"company_id": 1111111,
"member_job_title": "Senior Consultant",
"is_decision_maker": 1,
"member_job_description": "Senior Business analyst @ Company123",
"member_headline": "Healthcare Consultant",
"member_generated_headline": "Healthcare Consultant at Company 123",
"total_experience_duration": "2 years 4 months",
"total_experience_duration_months": 28,
﻿
Data point
Cleaning/enriching action 
company_id
Company ID from an active experience record from member_experience.
job_title
Special characters are removed.
total_experience_duration
Values converted to readable text.
total_experience_duration_months
Field aggregated from durationvalues.
The member_experience table is mapped with our historical data due to professional network hiding the work experience on certain employees' profiles. 
Data point
Processing
Description
Data type 
member_experience
-
Employee's work experience
Array of objects
company_id
Raw
Workplace (company) identifier in our database
Integer
date_from
Cleaned
Employment start date 
String (date)
date_from_year
Cleaned
Employment start year 
Integer
date_from_month
Cleaned
Employment start month 
Integer
date_to
Cleaned
Employment end date
String (date)
date_to_year
Cleaned
Employment end year
Integer
date_to_month
Cleaned
Employment end month
Integer
company_url
Raw
Employee's workplace URL on professional network
String
company_name
Raw
Employer company
﻿
title
Raw
Job title
String
department
Enriched
Department the employee works in
String
management_level
Enriched
Employee's management level 
String
description
Cleaned
Job description
String
order_in_profile
Raw
Record order as seen on the employee's profile
Integer
duration
Enriched
Employment duration
String (date)
duration_months
Cleaned
Employment duration in months
Integer
location
Cleaned
Job/workplace location
String
Experience
"member_experience": [
        {
            "company_id": 1774347,
            "date_from": "2015-10-01",
            "date_from_year": 2015,
            "date_from_month": 10,
            "date_to": "2016-09-01",
            "date_to_year": 2016,
            "date_to_month": 9,
            "company_name": "Company123, Ltd.",
            "company_url": "https://www.professional_network.com/company/company123",
            "title": "Senior Analyst",
            "description": "Financialconsulting for a leading manufacturing organizations.",
            "order_in_profile": 5,
            "duration": "1 year",
            "duration_months": 12,
            "department": "Project Management",
            "management_level": "Senior",
            "location": "Jacksonville, Florida Area"
        }
    ],
"member_experience": [
        {
            "company_id": 1774347,
            "date_from": "2015-10-01",
            "date_from_year": 2015,
            "date_from_month": 10,
            "date_to": "2016-09-01",
            "date_to_year": 2016,
            "date_to_month": 9,
            "company_name": "Company123, Ltd.",
            "company_url": "https://www.professional_network.com/company/company123",
            "title": "Senior Analyst",
            "description": "Financialconsulting for a leading manufacturing organizations.",
            "order_in_profile": 5,
            "duration": "1 year",
            "duration_months": 12,
            "department": "Project Management",
            "management_level": "Senior",
            "location": "Jacksonville, Florida Area"
        }
    ],
﻿
Data point
Cleaning/enriching action 
date_from
Value is converted to the yyyy-mm-dd format.
date_from_year
date_from_month
Year value extracted from date_from value
Value converted to integer.
date_to
Value is converted to the yyyy-mm-dd format.
date_to_year
date_to_month
Year value extracted from date_to value
Value converted to integer.
department
Enriched with our ML model from the title value.
management_level
Enriched with our ML model from the member_job_title value.
description
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Value is replaced to None if the description is shorter than 3 characters;
Text styling tags removed;
Multiple spaces are replaced with single ones.
duration
Derived from date_from and date_to values.
duration_months
Duration converted into a numerical value.
location
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
﻿
Data point
Processing
Description
Data type 
member_department
Enriched
Departments derived from the member_job_title
String
member_management_level
Enriched
Management levels identified from the member_job_title
String
is_working
Enriched
Represents if the employee is currently working
0 - the employee is currently not working
1 - the employee is currently working
Boolean
Experience
"member_department": "Project Management",
"member_management_level": "Senior",
"is_working": 1
"member_department": "Project Management",
"member_management_level": "Senior",
"is_working": 1
﻿
Data point
Cleaning/enriching action 
member_department
Enriched with our ML model from the member_job_title value.
member_subdepartment
Enriched with our ML model from the member_job_title value.
member_management_level
Enriched with our ML model from the member_job_title value.
is_working
Based on date_to and date_from values of employee experience. 
﻿
Education
Data point
Processing
Description
Data type 
member_education
﻿
Employee's education
Array of objects
major
Cleaned
Field of study
String
title
Cleaned
Educational institution
String
date_to
Cleaned
Graduation date
Integer
date_from
Cleaned
Enrolment date
Integer
institution_url
Cleaned
Institution's profile URL
String
description
Cleaned
Education description
String
activities_and_societies
Cleaned
Details about activities and societies
String
Education
 "member_education": [
        {
            "major": "Associate's degree, Business Administration and Management",
            "title": "Business College",
            "date_to": "2017",
            "date_from": "2015",
            "institution_url": "https://www.professional_network.com/school/business-college",
            "description": "Attended Business College from 2015 to 2017",
            "activities_and_societies": "Activities and Societies: Phi Theta Kappa"
        }
    ],
 "member_education": [
        {
            "major": "Associate's degree, Business Administration and Management",
            "title": "Business College",
            "date_to": "2017",
            "date_from": "2015",
            "institution_url": "https://www.professional_network.com/school/business-college",
            "description": "Attended Business College from 2015 to 2017",
            "activities_and_societies": "Activities and Societies: Phi Theta Kappa"
        }
    ],
﻿
Data point
Cleaning action
title
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Values are capitalized. 
major
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
date_from
Value is converted to the yyyy format.
date_to
Value is converted to the yyyy format.
institution_url
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
description
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
Text styling tags are removed; 
Multiple spaces are replaced with single ones.
activities_and_societies
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
Text styling tags are removed; 
Multiple spaces are replaced with single ones.
﻿
Hidden collections
Data point
Description
Data type 
is_hidden
Marks if the employee profile has a hidden education/experience collection.

0 - education/experience information was available at the time of profile scraping.
1 - education/experience information was not available at the time of profile scraping
Boolean
is_hidden + experience
is_hidden + education
"member_education": [
        {
            "title": "Harvard Law School",
            "major": null,
            "date_from": null,
            "date_to": null
        }
    ],
"is_working": 1,
"member_education": [
        {
            "title": "Harvard Law School",
            "major": null,
            "date_from": null,
            "date_to": null
        }
    ],
"is_working": 1,
﻿
﻿
Location
Data point
Processing
Description
Data type 
member_location_raw_address
Cleaned
Raw address of the employee's location
String
member_location_country
Cleaned
Country of the employee's location
String
member_location_regions
Cleaned
Geographical regions within the employee's country
Array of strings
Location
    "member_location_raw_address": "Nashville Metropolitan Area United States",
    "member_location_country": "United States",
    "member_location_regions": "[Northern America, Northern America, AMER]",
    "member_location_raw_address": "Nashville Metropolitan Area United States",
    "member_location_country": "United States",
    "member_location_regions": "[Northern America, Northern America, AMER]",
﻿
Data point
Cleaning action
location_raw_address
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Special trailed characters are trimmed; 
Value is set to None if it is shorter than three characters; 
The value of member_location_country is added at the end of the string.
location_country
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None. 
﻿
Recommendations and connections
Data point
Processing
Description
Data type 
member_recommendations
Cleaned
Employee recommendations
Array of objects
recommendation
Cleaned
Recommendation text
String
referee_name
Raw
Referee's name
String
referee_url
Raw
Referee's profile URL
String
recommendations_count
Cleaned
Number of received recommendations
Integer
connections_count
Raw
Number of employee's connections
Integer
Recommendations and connections
"member_recommendations": [
    {
      "recommendation": "“John was a great asset in collaborating the tasks in different departments to produce the same goal. He was great at providing advice and asking questions to avoid even a tiny error during the process. Great to work with him!”",
      "referee_name": "Marry Doe",
      "referee_url": "www.professional_network.com/in/marry-doe",
      "order_in_profile": 1
    }
  ],
  "recommendations_count": 1,
  "connections_count": 15535,
"member_recommendations": [
    {
      "recommendation": "“John was a great asset in collaborating the tasks in different departments to produce the same goal. He was great at providing advice and asking questions to avoid even a tiny error during the process. Great to work with him!”",
      "referee_name": "Marry Doe",
      "referee_url": "www.professional_network.com/in/marry-doe",
      "order_in_profile": 1
    }
  ],
  "recommendations_count": 1,
  "connections_count": 15535,
﻿
Data point
Cleaning action
member_recommendations
Deleted rows are filtered out. 
recommendation
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Value is set to None if it is shorter than three characters; 
Text styling tags are removed; 
Multiple spaces are replaced with single ones; 
Empty recommendations are filtered out. 
member_recommendations_count
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
None values are replaced with 0 and made an integer. 
﻿
Languages
Data point
Processing
Description
Data type 
member_languages
﻿
Employee's language knowledge
Array of objects
language
Cleaned
Language
String
proficiency
Cleaned
Language proficiency
String
order_in_profile
Raw
Record order in the section
Integer
Languages
"member_languages": [
        {
            "language": "English",
            "proficiency": "Intermediate",
            "order_in_profile": 1
        }
    ],
"member_languages": [
        {
            "language": "English",
            "proficiency": "Intermediate",
            "order_in_profile": 1
        }
    ],
﻿
Data point
Cleaning action
language
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
proficiency
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
﻿
Certifications
Data point
Processing
Description
Data type 
member_certifications
﻿
Employee's certifications
Array of objects
title
Cleaned
Language
String
issuer
Cleaned
Language proficiency
String
credential_id
Cleaned
Record order in the section
Integer
certificate_url
Cleaned
Certificate URL
String
date_from
Cleaned
Issue date 
String
date_to
Cleaned
Expiration date
String
issuer_url
Cleaned
Issuer profile URL
String
order_in_profile
Raw
Section record order
Integer
date_from_year
Cleaned
Issue year
Integer
date_from_month
Cleaned
Issue month
Integer
date_to_year
Cleaned
Expiration year
Integer
date_to_month
Cleaned
Expiration month
Integer
Certifications
"member_certifications": [
        {
            "title": "Data Analysis Certification B4",
            "issuer": "Data School123",
            "credential_id": 1345,
            "certificate_url": "http://data-analysis-certification-school123.com/verify?trk=public_profile_certification-title",
            "date_from": "2021-06-01",
            "date_to": "2024-06-01",
            "issuer_url": "https://www.professional_network.com/company/data-school-123",
            "order_in_profile": 1,
            "date_from_year": 2021,
            "date_from_month": 6,
            "date_to_year": 2024,
            "date_to_year": 6
        }
    ],
"member_certifications": [
        {
            "title": "Data Analysis Certification B4",
            "issuer": "Data School123",
            "credential_id": 1345,
            "certificate_url": "http://data-analysis-certification-school123.com/verify?trk=public_profile_certification-title",
            "date_from": "2021-06-01",
            "date_to": "2024-06-01",
            "issuer_url": "https://www.professional_network.com/company/data-school-123",
            "order_in_profile": 1,
            "date_from_year": 2021,
            "date_from_month": 6,
            "date_to_year": 2024,
            "date_to_year": 6
        }
    ],
﻿
Data point
Cleaning action
title
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
issuer
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
date_from
Value is converted to the yyyy-mm-dd format.
date_to
Value is converted to the yyyy-mm-dd format.
issuer_url
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
date_from_year
date_to_year
Year value from date is converted to an integer.
date_from_month
date_to_month
Month value from date is converted to an integer.
﻿
Courses
Data point
Processing
Description
Data type 
member_courses
﻿
Attended courses
Array of objects
organizer
Cleaned
Course organizer
String
title
Cleaned
Course title
String
order_in_profile
Raw
Record order in the section
Integer
Courses
 "member_courses": [
        {
            "organizer": "IT Academy",
            "title": "Microsoft Certified Excel Expert",
            "order_in_profile": 1
        }
    ],
 "member_courses": [
        {
            "organizer": "IT Academy",
            "title": "Microsoft Certified Excel Expert",
            "order_in_profile": 1
        }
    ],
﻿
Data point
Cleaning action
organizer
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
title
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
﻿
Awards
Data point
Processing
Description
Data type 
member_awards
﻿
Held awards
Array of objects
title
Cleaned
Award
String
issuer
Cleaned
Award issuer
String
description
Cleaned
Award description
String
date
Cleaned
Issue date 
String
order_in_profile
Raw
Section record order
Integer
date_year
Cleaned
Issue year
Integer
date_month
Cleaned
Issue month
Integer
Awards
"member_awards": [
        {
            "title": "Certified in Inventory Management",
            "issuer": "School of Operations Management",
            "description": "Certification in Production and Inventory Management",
            "date": "2001-01-01",
            "order_in_profile": 5,
            "date_year": 2001,
            "date_month": 4
        }
    ],
"member_awards": [
        {
            "title": "Certified in Inventory Management",
            "issuer": "School of Operations Management",
            "description": "Certification in Production and Inventory Management",
            "date": "2001-01-01",
            "order_in_profile": 5,
            "date_year": 2001,
            "date_month": 4
        }
    ],
﻿
Data point
Cleaning action
title
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Values are capitalized. 
issuer
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
date
Value is converted to the yyyy-mm-dd format.
date_year
Year value from date is converted to an integer.
date_month
Month value from date is converted to an integer.
﻿
Activity
Data point
Processing
Description
Data type 
member_activity
﻿
Interaction with posts on professional network
Array of objects
activity_url
Raw
Post URL
String
title
Cleaned
Post title
String
action
Cleaned
Interaction type
String
order_in_profile
Raw
Section record order
Integer
Activity
"member_activity": [
        {
            "activity_url": "https://www.professional_network.com/posts/company123-incorporated_healthcare-laborproductivity-activity-7161365554581172224-XUpZ",
            "title": "Company 123 is excited to introduce our Team Spotlight featuring John Doe! @Health Systems, Ltd #Healthcare #LaborProductivity",
            "action": "Liked by",
            "order_in_profile": 1
        }
    ],
"member_activity": [
        {
            "activity_url": "https://www.professional_network.com/posts/company123-incorporated_healthcare-laborproductivity-activity-7161365554581172224-XUpZ",
            "title": "Company 123 is excited to introduce our Team Spotlight featuring John Doe! @Health Systems, Ltd #Healthcare #LaborProductivity",
            "action": "Liked by",
            "order_in_profile": 1
        }
    ],
﻿
Data point
Cleaning action 
title
﻿
Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None;
Text styling tags removed;
Multiple spaces are replaced with single ones.
﻿

Data point	Processing	Description	Data type
member_last_updated	Cleaned	Date the record was last updated	String
member_is_deleted	Raw	Indicates whether the profile was accessible: 1 - deleted or private 0 - publicly available	Integer

Data point	Cleaning action
member_last_updated	Value is converted to the yyyy-mm-dd format.

Data point	Processing	Description	Data type
member_id	Raw	Identification key in our database	Integer
member_websites_professional_network	Raw	Professional network profile URL	String
member_picture_url	Raw	Profile picture URL	String
member_full_name	Cleaned	Full name	String
member_name_first	Raw	First name	String
member_name_middle	Enriched	Middle name	String
member_name_last	Enriched	Last name	String
member_shorthand_names	Raw	A list of all historical employee shorthand names	Array of strings
member_follower_count	Raw	Number of profile followers	Integer

Data point	Cleaning action
member_full_name	Special characters/emojis are removed; Any words that follow a comma or in parentheses are removed; Titles (preceding or following the name) are removed.
member_name_middle	Parsed from member_full_name.
member_name_last	Parsed from member_full_name.

Data point	Processing	Description	Data type
member_skills	Enriched	List of employees' skills	Array of strings

Data point	Enriching action
member_skills	Enriched with our ML model from different description fields.

Data point	Processing	Description	Data type
member_description	Raw	Job position description	String
company_id	Enriched	Identification key for the company associated with the employee's experience	Integer
member_job_title	Cleaned	Current job position title	String
is_decision_maker	Enriched	Indicates whether the employee is a decision-maker based on member_job_title 1 - Employee is marked as a decision-maker in the current role 0 - Employee is not marked as a decision-maker in the current role	Integer
member_job_description	Raw	Current job position description	String
member_headline	Raw	Job title found in the profile headline	String
member_generated_headline	Raw	A user-written headline that can be found in web search, also viewed and other publicly available spaces. It serves the same purpose as the title but is derived from a different source, potentially providing more accurate and up-to-date profile information. This field should be used in place title as it reflects the latest user activity.	String
total_experience_duration	Enriched	Summed up experience (displayed as years and months)	String
total_experience_duration_months	Enriched	Summed up employee experience (displayed as months)	Integer

Data point	Cleaning/enriching action
company_id	Company ID from an active experience record from member_experience.
job_title	Special characters are removed.
total_experience_duration	Values converted to readable text.
total_experience_duration_months	Field aggregated from durationvalues.

Data point	Processing	Description	Data type
member_experience	-	Employee's work experience	Array of objects
company_id	Raw	Workplace (company) identifier in our database	Integer
date_from	Cleaned	Employment start date	String (date)
date_from_year	Cleaned	Employment start year	Integer
date_from_month	Cleaned	Employment start month	Integer
date_to	Cleaned	Employment end date	String (date)
date_to_year	Cleaned	Employment end year	Integer
date_to_month	Cleaned	Employment end month	Integer
company_url	Raw	Employee's workplace URL on professional network	String
company_name	Raw	Employer company
title	Raw	Job title	String
department	Enriched	Department the employee works in	String
management_level	Enriched	Employee's management level	String
description	Cleaned	Job description	String
order_in_profile	Raw	Record order as seen on the employee's profile	Integer
duration	Enriched	Employment duration	String (date)
duration_months	Cleaned	Employment duration in months	Integer
location	Cleaned	Job/workplace location	String

Data point	Cleaning/enriching action
date_from	Value is converted to the yyyy-mm-dd format.
date_from_year date_from_month	Year value extracted from date_from value Value converted to integer.
date_to	Value is converted to the yyyy-mm-dd format.
date_to_year date_to_month	Year value extracted from date_to value Value converted to integer.
department	Enriched with our ML model from the title value.
management_level	Enriched with our ML model from the member_job_title value.
description	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None; Value is replaced to None if the description is shorter than 3 characters; Text styling tags removed; Multiple spaces are replaced with single ones.
duration	Derived from date_from and date_to values.
duration_months	Duration converted into a numerical value.
location	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.

Data point	Processing	Description	Data type
member_department	Enriched	Departments derived from the member_job_title	String
member_management_level	Enriched	Management levels identified from the member_job_title	String
is_working	Enriched	Represents if the employee is currently working 0 - the employee is currently not working 1 - the employee is currently working	Boolean

Data point	Cleaning/enriching action
member_department	Enriched with our ML model from the member_job_title value.
member_subdepartment	Enriched with our ML model from the member_job_title value.
member_management_level	Enriched with our ML model from the member_job_title value.
is_working	Based on date_to and date_from values of employee experience.

Data point	Processing	Description	Data type
member_education		Employee's education	Array of objects
major	Cleaned	Field of study	String
title	Cleaned	Educational institution	String
date_to	Cleaned	Graduation date	Integer
date_from	Cleaned	Enrolment date	Integer
institution_url	Cleaned	Institution's profile URL	String
description	Cleaned	Education description	String
activities_and_societies	Cleaned	Details about activities and societies	String

Data point	Cleaning action
title	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None; Values are capitalized.
major	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
date_from	Value is converted to the yyyy format.
date_to	Value is converted to the yyyy format.
institution_url	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
description	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None. Text styling tags are removed; Multiple spaces are replaced with single ones.
activities_and_societies	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None. Text styling tags are removed; Multiple spaces are replaced with single ones.

Data point	Description	Data type
is_hidden	Marks if the employee profile has a hidden education/experience collection. 0 - education/experience information was available at the time of profile scraping. 1 - education/experience information was not available at the time of profile scraping	Boolean

Data point	Processing	Description	Data type
member_location_raw_address	Cleaned	Raw address of the employee's location	String
member_location_country	Cleaned	Country of the employee's location	String
member_location_regions	Cleaned	Geographical regions within the employee's country	Array of strings

Data point	Cleaning action
location_raw_address	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None; Special trailed characters are trimmed; Value is set to None if it is shorter than three characters; The value of member_location_country is added at the end of the string.
location_country	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.

Data point	Processing	Description	Data type
member_recommendations	Cleaned	Employee recommendations	Array of objects
recommendation	Cleaned	Recommendation text	String
referee_name	Raw	Referee's name	String
referee_url	Raw	Referee's profile URL	String
recommendations_count	Cleaned	Number of received recommendations	Integer
connections_count	Raw	Number of employee's connections	Integer

Data point	Cleaning action
member_recommendations	Deleted rows are filtered out.
recommendation	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None; Value is set to None if it is shorter than three characters; Text styling tags are removed; Multiple spaces are replaced with single ones; Empty recommendations are filtered out.
member_recommendations_count	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None; None values are replaced with 0 and made an integer.

Data point	Processing	Description	Data type
member_languages		Employee's language knowledge	Array of objects
language	Cleaned	Language	String
proficiency	Cleaned	Language proficiency	String
order_in_profile	Raw	Record order in the section	Integer

Data point	Cleaning action
language	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
proficiency	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.

Data point	Processing	Description	Data type
member_certifications		Employee's certifications	Array of objects
title	Cleaned	Language	String
issuer	Cleaned	Language proficiency	String
credential_id	Cleaned	Record order in the section	Integer
certificate_url	Cleaned	Certificate URL	String
date_from	Cleaned	Issue date	String
date_to	Cleaned	Expiration date	String
issuer_url	Cleaned	Issuer profile URL	String
order_in_profile	Raw	Section record order	Integer
date_from_year	Cleaned	Issue year	Integer
date_from_month	Cleaned	Issue month	Integer
date_to_year	Cleaned	Expiration year	Integer
date_to_month	Cleaned	Expiration month	Integer

Data point	Cleaning action
title	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
issuer	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
date_from	Value is converted to the yyyy-mm-dd format.
date_to	Value is converted to the yyyy-mm-dd format.
issuer_url	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
date_from_year date_to_year	Year value from date is converted to an integer.
date_from_month date_to_month	Month value from date is converted to an integer.

Data point	Processing	Description	Data type
member_courses		Attended courses	Array of objects
organizer	Cleaned	Course organizer	String
title	Cleaned	Course title	String
order_in_profile	Raw	Record order in the section	Integer

Data point	Cleaning action
organizer	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
title	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.

Data point	Processing	Description	Data type
member_awards		Held awards	Array of objects
title	Cleaned	Award	String
issuer	Cleaned	Award issuer	String
description	Cleaned	Award description	String
date	Cleaned	Issue date	String
order_in_profile	Raw	Section record order	Integer
date_year	Cleaned	Issue year	Integer
date_month	Cleaned	Issue month	Integer

Data point	Cleaning action
title	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None; Values are capitalized.
issuer	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None.
date	Value is converted to the yyyy-mm-dd format.
date_year	Year value from date is converted to an integer.
date_month	Month value from date is converted to an integer.

Data point	Processing	Description	Data type
member_activity		Interaction with posts on professional network	Array of objects
activity_url	Raw	Post URL	String
title	Cleaned	Post title	String
action	Cleaned	Interaction type	String
order_in_profile	Raw	Section record order	Integer

Data point	Cleaning action
title	Values ["None"; "Unknown"; "NaN"; "nan"; "na"; "null"; "Null"; "NULL"; "-"; "--"] are replaced with value None; Text styling tags removed; Multiple spaces are replaced with single ones.

Updated 20 Mar 2025

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Clean Employee Data

Base Employee Data

Dictionary: Clean Employee Data

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Overview

Metadata

Data sample

Cleaning actions

Identifiers

Data sample

Cleaning actions

Skills

Data sample

Enriching action

Experience

Data sample

Cleaning and enriching actions

Data sample

Cleaning and enriching actions

Data sample

Enriching actions

Education

Data sample

Cleaning actions

Hidden collections

Data sample

Location

Data sample

Cleaning actions

Recommendations and connections

Data sample

Cleaning actions

Languages

Data sample

Cleaning actions

Certifications

Data sample

Cleaning actions

Courses

Data sample

Cleaning actions

Awards

Data sample

Cleaning actions

Activity

Data sample

Cleaning actions