Integrate the Greenhouse API with the Go API

Setup the Greenhouse API trigger to run a workflow which integrates with the Go API. Pipedream's integration platform allows you to integrate Greenhouse and Go remarkably fast. Free for developers.

Popular Greenhouse and Go Actions#

Run Go Code with the Go API

Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.

Try it

Overview of Greenhouse#

The Greenhouse API offers a powerful suite of tools for automating and enhancing the recruitment process. It allows you to programmatically access candidate information, job listings, scorecards, and scheduling details, which opens a myriad of possibilities for streamlining recruiting workflows. By leveraging the Greenhouse API on Pipedream, you can automate repetitive tasks, integrate with other HR systems, analyze recruitment data, and build custom event-driven workflows to improve the efficiency and effectiveness of your hiring process.

Connect Greenhouse#

import { axios } from "@pipedream/platform"
export default defineComponent({
  props: {
    greenhouse: {
      type: "app",
      app: "greenhouse",
    }
  },
  async run({steps, $}) {
    return await axios($, {
      url: `https://harvest.greenhouse.io/v1/user_roles`,
      auth: {
        username: `${this.greenhouse.$auth.api_key}`,
        password: ``,
      },
    })
  },
})

Overview of Go#

You can execute custom Go scripts on-demand or in response to various triggers and integrate with thousands of apps supported by Pipedream. Writing with Go on Pipedream enables backend operations like data processing, automation, or invoking other APIs, all within the Pipedream ecosystem. By leveraging Go's performance and efficiency, you can design powerful and fast workflows to streamline complex tasks.

Connect Go#

package main

import (
	"fmt"

	pd "github.com/PipedreamHQ/pipedream-go"
)

func main() {
	// Access previous step data using pd.Steps
	fmt.Println(pd.Steps)

	// Export data using pd.Export
	data := make(map[string]interface{})
	data["name"] = "Luke"
	pd.Export("data", data)
}