greytHR connector

Set up the greytHR connector in Kaivo: authentication, configuration, the 11 BigQuery tables it syncs, and answers to common questions.

Written By Lauri Raivio

Last updated 16 days ago

Kaivo is a fully managed data platform that syncs your greytHR data into a Google BigQuery warehouse and keeps it up to date automatically. There is no pipeline to build and no infrastructure to run, so you can spend your time analysing your HR data instead of moving it.

What is the greytHR connector

Sync your GreytHR data into BigQuery with Kaivo to report on headcount, employee details, and HR records without exporting spreadsheets.

Category	HR
Authentication	Username and password
Setup	Self-service

Getting started with the greytHR connector

Sign up for Kaivo and create a workspace.
Connect your greytHR account.
Choose which tables to sync.
Wait for the initial sync to finish.
Query your data in BigQuery or your favourite AI or BI tool.

Authenticating greytHR

Connect with your greytHR login. You provide:

Field	Description
Password

Configuring the greytHR connector

When you set up the connector, you provide:

Field	Description
Host URL	Your GreytHR Host URL
Base URL	The base URL for the GreytHR API, e.g. https://api.greythr.com
Username

Tables and columns synced from greytHR

Kaivo syncs 11 tables from greytHR into a dedicated dataset in your BigQuery warehouse. Click any table to see its columns and types.

Employee Bank Details (8 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`bank_name`	`FLOAT64`
`bank_branch`	`FLOAT64`
`branch_code`	`STRING`
`employee_id`	`FLOAT64`
`bank_account_number`	`STRING`
`salary_payment_mode`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Employee PF & ESI details (11 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`uan`	`STRING`
`pf_number`	`STRING`
`employee_id`	`FLOAT64`
`pf_eligible`	`BOOL`
`esi_eligible`	`BOOL`
`pf_kyc_linked_on`	`STRING`
`pf_existing_member`	`BOOL`
`excess_epf_contribution`	`BOOL`
`excess_eps_contribution`	`BOOL`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Employee Qualifications Details (11 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`id`	`FLOAT64`
`duration`	`STRING`
`employee`	`FLOAT64`
`qual_area`	`FLOAT64`
`qual_year`	`FLOAT64`
`institute`	`STRING`
`qual_level`	`FLOAT64`
`qual_description`	`FLOAT64`
`qual_completion_year`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Employee Separation Details (12 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`left_org`	`BOOL`
`employee_id`	`FLOAT64`
`leaving_date`	`STRING`
`leaving_reason`	`FLOAT64`
`fit_to_be_rehired`	`BOOL`
`submission_date`	`STRING`
`exit_interview_date`	`STRING`
`final_settlement_date`	`STRING`
`submitted_resignation`	`BOOL`
`tentative_leaving_date`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Employee Statutory Details (11 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`disabled`	`BOOL`
`birthplace`	`STRING`
`employee_id`	`FLOAT64`
`expatriate`	`BOOL`
`father_name`	`STRING`
`is_director`	`BOOL`
`nationality`	`FLOAT64`
`country_of_origin`	`FLOAT64`
`residential_status`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Employees (19 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`name`	`STRING`
`email`	`STRING`
`title`	`STRING`
`gender`	`STRING`
`mobile`	`STRING`
`status`	`FLOAT64`
`leftorg`	`BOOL`
`last_name`	`STRING`
`first_name`	`STRING`
`date_of_join`	`STRING`
`employee_id`	`FLOAT64`
`employee_no`	`STRING`
`date_of_birth`	`STRING`
`leaving_date`	`STRING`
`last_modified`	`STRING`
`personal_email`	`STRING`
`probation_period`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Employees Categories (3 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`employee_id`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Column	Type	Description
`_kaivo_parent_id`	`STRING`	Foreign key referencing _kaivo_id in the parent table. Auto-generated by Kaivo.
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`id`	`FLOAT64`
`value`	`FLOAT64`
`category`	`FLOAT64`
`value_desc`	`STRING`
`effective_to`	`STRING`
`category_desc`	`STRING`
`effective_from`	`STRING`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Employees Personal Details (6 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`blood_group`	`FLOAT64`
`employee_id`	`FLOAT64`
`spouse_name`	`STRING`
`marital_status`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Employees Profile (9 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`twitter`	`STRING`
`wish_dob`	`STRING`
`facebook`	`STRING`
`linked_in`	`STRING`
`nickname`	`STRING`
`biography`	`STRING`
`employee_id`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Employees Work Details (7 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`employee_id`	`FLOAT64`
`confirm_date`	`STRING`
`notice_period`	`FLOAT64`
`onboarding_status`	`STRING`
`extended_probation_days`	`FLOAT64`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

Users List (9 columns)

Column	Type	Description
`_kaivo_id`	`STRING`	Primary key that uniquely identifies the row. Auto-generated by Kaivo.
`type`	`FLOAT64`
`id`	`FLOAT64`
`admin`	`BOOL`
`email`	`STRING`
`deleted`	`BOOL`
`user_name`	`STRING`
`system_password`	`BOOL`
`_kaivo_extracted_at`	`TIMESTAMP`	Timestamp that shows when the row was extracted. Auto-generated by Kaivo.

How the greytHR sync works

After the first load, Kaivo keeps your BigQuery warehouse up to date for you. Where greytHR supports it, each sync pulls only new and changed records so it stays fast; otherwise it refreshes the whole table. Every record keeps its original ID, so you won't get duplicate rows.

Frequently asked questions

How long does the initial sync take for greytHR?

It depends on how much history is in your greytHR account. Most initial syncs finish within minutes, while large accounts can take a few hours. After that, syncs only fetch new and changed records, so they're much faster.

Can I sync only some tables or columns?

Yes. You pick which tables to sync when you set up the connection and can change the selection later. Tables you don't select are never copied to your warehouse.

What happens when greytHR's schema changes?

New fields are never added automatically. You choose which fields to sync, so data you haven't selected (sensitive personal data, for example) never lands in your warehouse. When a new field appears, it becomes available for you to add. What happens to removed or renamed fields depends on a table's sync mode: full-refresh tables always match what's currently in greytHR, so dropped fields disappear, while incremental tables keep their existing columns and history, so an old field stays and newly added fields fill in over time.

Your data lives in your own Kaivo-managed BigQuery warehouse, so the most direct option is to delete or anonymise specific records right in BigQuery. If you delete data in greytHR instead, full-refresh tables drop it on the next sync, while incremental tables keep it, so you would remove the row in BigQuery or ask us to run a full refresh. To remove everything, delete the greytHR connector in Kaivo and all of its synced data is deleted with it.

Common use cases for greytHR data

Headcount reporting

Use Employees and Employees Categories to track headcount and structure over time.

Employee details

Join work details and personal details to build people reports.

Compliance view

Use statutory and bank details to support payroll and compliance reporting.

Use greytHR data in your AI and BI tools

Once greytHR data lands in your Kaivo-managed BigQuery warehouse, you can explore it with AI tools or any BI tool that connects to BigQuery. Here's how the most common destinations work with greytHR data.

Claude

Use Kaivo's MCP server to give Claude secure, workspace-scoped access to your data. Setup guide →

Power BI

Microsoft's BI tool with a native BigQuery connector. Supports direct query and scheduled refresh. Setup guide →

Data Studio

Free Google BI tool with native BigQuery support. One-click connection to your Kaivo warehouse; great for SMB teams on Google Workspace. Setup guide →

Tableau

The premium analytics standard, with native BigQuery integration. Setup guide →

Google Sheets

Use Connected Sheets to query BigQuery directly from a spreadsheet, with no SQL. Setup guide →

Excel

Connect via Power Query's BigQuery connector. Setup guide →

Metabase

Open-source BI tool with strong BigQuery support. Setup guide →

See our pricing page for greytHR connector pricing and plan details.

Greenhouse: Sync Greenhouse to BigQuery.
Kellokortti: Sync Kellokortti to BigQuery.
Nepton: Sync Nepton to BigQuery.
Teamtailor: Sync Teamtailor to BigQuery.
7shifts: Sync 7shifts to BigQuery.
Ashby: Sync Ashby to BigQuery.

Was this helpful?

Still need help? Share an idea