Employee sample data

Ready-made files to download

Generated from the schemas below with a fixed seed and committed to the site, so a link to one of these files keeps returning the same bytes. They are public domain (CC0) — use them in a tutorial, a test suite or a course without asking. Need different columns, more rows or another format? Build it above.

Sample employee records — data dictionary

These values illustrate schemas and import behavior, not real population or business distributions. Review nulls, units and date bounds before building assertions or charts.

The schema

An employee table that actually exercises a dashboard needs a mix of identifiers, categories, dates and numbers. This one takes about a minute to build in the generator:

Column          Field type              Options
------------------------------------------------------------
employee_id     Row Number
full_name       Full Name               locale: any
email           Email Address           unique
job_title       Job Title
department      Custom List             Engineering, Sales, Marketing,
                                        Finance, HR, Support, Operations
hire_date       Date                    from 2015-01-01, sequential
salary          Integer                 min 38000, max 185000
is_active       Boolean
city            City
country         Country
manager_id      Integer                 min 1, max 40
performance     Custom List             Exceeds, Meets, Developing

employee_id,full_name,job_title,department,hire_date,salary,is_active
1,Elena Rossi,QA Engineer,Engineering,2015-02-14,71000,true
2,Marco Schneider,Account Executive,Sales,2015-04-03,64500,true
3,Aisha Yılmaz,Data Analyst,Finance,2015-06-21,79200,false

Choices that make the data useful

A few decisions separate a dataset that demos well from one that falls apart on the first pivot:

• Departments as a Custom List, not a random text field. Grouping and filtering only mean something if the same seven values repeat. Repeat a value in the list to weight it — listing Engineering three times gives you a realistically large engineering team instead of seven equal departments.
• Hire dates sequential. Set the Date field's order to sequential and headcount grows smoothly over time, which is what a hiring chart should look like. A random draw produces a jagged mess.
• Salary as an Integer with a real range. Anchor the minimum and maximum to your actual bands. If you want a realistic long tail rather than a flat spread, use the Number (Normal Dist.) field instead.
• Blank % on optional columns. Manager, end date and secondary email should be empty for some rows — the CEO has no manager, and a dashboard that assumes otherwise breaks on row one.

Manager hierarchies

A manager_id drawn at random points at an arbitrary row, which is fine for a shape test and wrong for an org chart — it will produce cycles, and some employee will manage themselves. For a real hierarchy, generate the managers first as a small export, then feed their IDs into a Custom List on the employee schema so every report points at somebody who exists and is senior. The two-pass technique is the same one used for foreign keys in the database guide.

Where this data goes

• Spreadsheets and BI. Export CSV, or TSV if you want to paste straight into a sheet. Watch employee IDs with leading zeros — see the Excel guide.
• HR product demos. Generate once with a fixed seed so every screenshot and every demo shows the same people.
• Database seeding. SQL export gives inserts plus an optional CREATE TABLE; PostgreSQL and MySQL notes cover bulk loading.
• Training material. A fixed seed means every student works with identical data, so an exercise answer is the same for everyone.

Why not anonymised real data

Anonymising a payroll export is harder than it looks. Salary, job title, hire date and department together re-identify people in a small company even with names stripped, and an HR dataset is exactly the kind of thing that should never sit in a demo environment or a public repository. Generating from scratch avoids the problem rather than mitigating it — there is nothing to re-identify. The methodology page covers what each field draws from.

Common questions

Related

• Sales sample data — orders, revenue and a time series.
• Excel sample data — pivot tables and clean imports.
• Random name generator — the people behind the rows.
• Database test data — seeding and foreign keys.

Last updated 16 October 2026