Human Resources
21 columns
200 rows
8 formats

HR Diversity Equity Inclusion Metrics

This dataset provides detailed, flat-structured information on workforce composition for Diversity, Equity, and Inclusion (DEI) analytics. It captures employee demographics, employment status, roles, and reporting relationships, enabling organizations to benchmark and monitor DEI metrics across gender, ethnicity, age, and other key attributes. Ideal for HR analytics, compliance reporting, and strategic workforce planning.

last updated 12 Jul 2025
by GoMask

The brief that made it

Benchmarking gender and ethnicity representation across departments and roles

Sample rows

preview · 8 of 200 rows · all 21 columns

employee_idstring	pay_gradestring	ageinteger	is_activeboolean	location_countrystring	first_namestring	last_namestring	genderstring	date_of_birthdate	ethnicitystring	role_titlestring	departmentstring	employment_typestring	hire_datedate	manager_idstring	location_statestring	location_citystring	disability_statusstring	veteran_statusstring	lgbtq_statusstring	termination_datedate
E0001	L4	39	true	USA	Jack	Nguyen	Male	1985-03-12	Asian	Lead Software Engineer	Engineering	Full-time	2012-07-15	E0005	CA	San Francisco	No	No	No	blank
E0002	M1	27	true	USA	Priya	Sundar	Female	1997-06-28	Asian	Business Analyst	Business Intelligence	Full-time	2022-03-01	E0007	TX	Austin	No	No	Yes	blank
E0003	E2	64	true	USA	Lucas	Miller	Male	1959-10-21	White	VP of Engineering	Executive	Full-time	2010-01-10	E0009	WA	Seattle	No	Yes	No	blank
E0004	blank	20	true	Mexico	Camila	Martinez	Female	2003-11-13	Hispanic or Latino	Marketing Intern	Administration	Intern	2023-06-01	E0012	CDMX	Mexico City	No	No	Prefer not to say	blank
E0005	E1	47	true	USA	Olivia	Chen	Female	1977-02-19	Asian	Director of Engineering	Engineering	Full-time	2015-09-15	E0003	CA	San Francisco	No	No	No	blank
E0006	M2	34	true	Norway	Anders	Johansen	Male	1990-04-04	White	Data Scientist	Business Intelligence	Full-time	2019-08-20	E0007	Oslo	Oslo	No	No	No	blank
E0007	E2	50	true	UK	Fatima	Ali	Female	1974-01-20	Asian	Head of Business Intelligence	Business Intelligence	Full-time	2016-05-10	E0009	England	London	Prefer not to say	No	No	blank
E0008	blank	25	true	USA	Maddox	Smith	Non-binary	1999-06-22	Black or African American	Customer Support Rep	Customer Support	Part-time	2022-10-12	E0011	NY	New York	Yes	No	Yes	blank
E0009	E3	60	true	USA	Clara	Meyer	Female	1963-09-05	White	Chief Executive Officer	Executive	Full-time	2005-02-18	blank	IL	Chicago	No	No	No	blank
E0010	M1	34	true	Mexico	Javier	Lopez	Male	1989-11-02	Hispanic or Latino	Account Executive	Sales	Full-time	2018-01-08	E0013	JAL	Guadalajara	No	No	No	blank
E0011	L4	41	true	USA	Emily	Brown	Female	1983-05-29	White	Customer Support Manager	Customer Support	Full-time	2012-04-16	E0017	TX	Dallas	No	No	No	blank
E0012	C1	32	true	Poland	Mateusz	Kowalski	Male	1992-01-10	White	HR Specialist	Human Resources	Full-time	2021-09-30	E0015	Mazowieckie	Warsaw	No	No	No	blank
E0013	E1	48	true	Ireland	Connor	O'Connor	Male	1975-12-07	White	Head of Sales	Sales	Full-time	2013-11-01	E0009	Leinster	Dublin	Prefer not to say	No	No	blank
E0014	C1	27	true	USA	Liam	Ramirez	Male	1996-09-18	Hispanic or Latino	Sales Development Rep	Sales	Full-time	2023-01-05	E0013	FL	Miami	No	No	Yes	blank
E0015	M2	41	true	Poland	Marina	Ivanova	Female	1982-08-15	White	HR Manager	Human Resources	Full-time	2016-09-15	E0020	Mazowieckie	Warsaw	No	No	No	blank
E0016	M1	37	true	Canada	Jordan	Brooks	Other	1987-02-05	Two or More Races	Finance Analyst	Finance	Full-time	2020-04-10	E0021	ON	Toronto	No	No	Prefer not to say	blank
E0017	E1	43	true	Japan	Haruka	Tanaka	Female	1980-10-02	Asian	Customer Support Director	Customer Support	Full-time	2011-03-14	E0009	Tokyo	Tokyo	No	No	No	blank
E0018	M1	28	true	USA	Nathan	Lee	Male	1995-07-12	Asian	Support Engineer	IT	Full-time	2021-01-15	E0019	CA	San Jose	No	No	No	blank
E0019	E1	53	true	Poland	Tomasz	Nowak	Male	1970-06-23	White	Director of IT	IT	Full-time	2017-07-01	E0003	Wielkopolskie	Poznan	Prefer not to say	No	No	blank
E0020	E3	55	true	USA	Grace	Anderson	Female	1968-12-30	Black or African American	Chief People Officer	Executive	Full-time	2010-04-12	E0009	NY	New York	No	No	Yes	blank

What the 200 rows show

from the 200-row sample

E3 (pay grade) stands out: mean age is 60.1, against 37.9 for the rest.

99%is_active = true
35.5median age
3disability statuses
3veteran statuses
3lgbtq statuses
5genders

Mean age by pay_grade156 rows

age200 rows, in bands of 10

Median 35.5, from 19 to 70.

location_country200 rows · top 10 of 23 values

USA97
Norway15
Mexico12
Poland8
Japan8
Canada7
France7
Germany7
Ireland6
Brazil5

21 columns by typefrom the column list below

string 16
integer 1
date 3
boolean 1

Columns

21 columns in four groups

blueprint · 21 columns
column	type	description	example
Text 16 columns
`employee_id`	string	Unique identifier for each employeeunique	E0001
`first_name`	string	Employee's first name	Jack
`last_name`	string	Employee's last name	Nguyen
`gender`	string	Employee's self-identified genderMale · Female · Non-binary · Prefer not to say · Other	Male
`ethnicity`	string	Employee's self-identified ethnicity9 values	Asian
`role_title`	string	Employee's job title or role within the organization	Lead Software Engineer
`department`	string	Department or business unit the employee belongs to9 departments	Engineering
`employment_type`	string	Type of employment (e.g., full-time, part-time, contractor, intern)Full-time · Part-time · Contractor · Intern · Temporary	Full-time
`manager_id`	string	Unique identifier of the employee's direct manager (references employee_id)optional	E0005
`location_country`	string	Country where the employee is based	USA
`location_state`	string	State or province where the employee is basedoptional	CA
`location_city`	string	City where the employee is basedoptional	San Francisco
`disability_status`	string	Employee's self-identified disability statusYes · No · Prefer not to say · optional	No
`veteran_status`	string	Employee's self-identified veteran statusYes · No · Prefer not to say · optional	No
`lgbtq_status`	string	Employee's self-identified LGBTQ+ statusYes · No · Prefer not to say · optional	No
`pay_grade`	string	Employee's pay grade or compensation band8 grades · optional	L4
Numbers 1 column
`age`	integer	Employee's age in years (calculated from date_of_birth)15 to 100 · optional	39
Dates and times 3 columns
`date_of_birth`	date	Employee's date of birth	1985-03-12
`hire_date`	date	Date the employee was hired	2012-07-15
`termination_date`	date	Date the employee left the organization, if applicableoptional	2024-02-01
True or false 1 column
`is_active`	boolean	Indicates if the employee is currently active (true) or not (false)	true

Use it for

A human resources dashboard
The is_active rate, age by pay_grade and a breakdown of location_country. Excel, Power BI or Tableau.
Why do the 16 E3 rows have a mean age of 60.1?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Employees200
E000139L4
E000227M1
E004961E3
A software demo
Believable employees with first_name, last_name and gender to fill a screen in front of a buyer.

This dataset200 rows21 columns

Yours1,000 rows21 columnslocation_country: UK only

blueprint · hr-diversity-equity-inclusion-metrics

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 21 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Departments with <5 employees excluded
Sensitive fields anonymized
Data snapshot taken quarterly
Promotions and exits tracked

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: hr-diversity-equity-inclusion-metrics

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

A human resources dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Human Resources

What should your data show?