Are these real employees?

No. Every name, salary, and date is randomly generated locally in your browser from sample pools. The data is fully synthetic and never leaves your device.

What fields are produced?

Each record has an employee ID, full name, email, department, job title, a salary appropriate to the role, hire date, and a manager ID. The first employees are seeded as managers so the manager links resolve.

Do the manager references form a valid org chart?

Yes, loosely. The first few generated employees are treated as managers, and everyone else is assigned one of their IDs. This gives a single tier of management you can render as a basic org chart.

How are salaries calculated?

Each job title has a realistic salary band, and the salary is drawn at random within that band. Senior and engineering roles sit higher than entry-level or support roles, so analytics demos look plausible.

Can I generate the same set twice?

No. Each generation is independently random. If you need a fixed dataset for tests, generate once and save the output.

What is the Sample HR Dataset Generator?

Creates fake employee datasets with names, departments, job titles, salaries, hire dates, and managers as CSV or JSON. Ideal for testing HR software, org-chart tools, payroll systems, and people-analytics dashboards. It runs free in your browser on Gera Tools, with nothing uploaded.

Sample HR Dataset Generator

Name: Sample HR Dataset Generator
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

This generator builds realistic but completely fake employee records so you can test HRIS software, org-chart tools, payroll imports, and people-analytics dashboards without using real personnel data. Everything is created locally in your browser.

How it works

Each employee record is assembled from sample pools, with a light org structure:

An employee ID like E-0042, a name, and a derived email such as [email protected].
A department and a job title drawn from that department, where each title carries a realistic salary band; the final salary is random within the band.
A hire date placed within the past ten years.
A manager ID: the first few records are seeded as managers (no manager), and everyone else is linked to one of those manager IDs, forming a single reporting tier.

Output is CSV with a header row or pretty-printed JSON.

What the generated records look like

A sample of generated CSV rows (values are illustrative — generated fresh each time):

employee_id,name,email,department,job_title,salary,hire_date,manager_id
E-0001,Sarah Mitchell,[email protected],Engineering,VP Engineering,145000,2019-03-12,
E-0002,James Okafor,[email protected],Engineering,Senior Engineer,112000,2020-07-28,E-0001
E-0003,Priya Nair,[email protected],Marketing,Marketing Manager,88000,2021-01-15,E-0004
E-0042,Tom Walker,[email protected],Support,Customer Success Rep,52000,2023-06-09,E-0005

The first few employees are managers (no manager_id); everyone else links to one of those IDs, producing a valid single-tier hierarchy.

Salary bands by role type

Salaries are randomly drawn from ranges appropriate to each role:

Role tier	Approximate salary range
C-suite / VP	$120,000 – $180,000
Senior / Lead	$90,000 – $130,000
Mid-level	$65,000 – $95,000
Entry-level	$42,000 – $68,000
Support / Ops	$38,000 – $60,000

These ranges are illustrative US market figures used to make analytics demos look realistic. They vary by department: Engineering roles sit higher, Support and Admin lower, matching typical compensation structures.

What you can build with it

HRIS prototypes: Import the CSV into an HRIS tool to demo employee directories, search, and filtering without real data.
Org chart rendering: The manager_id column resolves to real employee IDs, so you can render a D3.js or Mermaid org chart immediately.
Payroll system testing: Test import flows, field validation, and edge cases (missing managers, different departments, date ranges).
People analytics dashboards: Build department headcount charts, tenure analysis, salary equity comparisons, and attrition-by-department views.
Export and ETL demos: Show a data pipeline that takes HR data, transforms it, and loads it into a data warehouse — useful for tool demos without exposing real employee records.

Privacy considerations

This data is entirely synthetic. Names are randomly generated combinations, emails use @example.com (a reserved domain that cannot receive mail), and no real person’s data is included. It is safe to use in demos, staging environments, screenshots, and documentation without any privacy risk. Each generation is independently random — save the file if you need a stable fixture.