Lever to Metabase

This page provides you with instructions on how to extract data from Lever and analyze it in Metabase. (If the mechanics of extracting data from Lever seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Lever?

Lever is a cloud-based recruiting software platform. It provides tools to source, nurture, interview, and hire job candidates, with both an applicant tracking system (ATS) and CRM functionality, along with analytics that provide insights into things such as cost and engagement.

What is Metabase?

Metabase provides a visual query builder that lets users generate simple charts and dashboards, and supports SQL for gathering data for more complex business intelligence visualizations. It runs as a JAR file, and its developers make it available in a Docker container and on Heroku and AWS. Metabase is free of cost and open source, licensed under the AGPL.

Getting data out of Lever

Lever provides a RESTful API that lets developers fetch information about postings, candidates, interviews, and other items stored in the platform. For example, to retrieve information about a specific posting, you would call GET https://api.lever.co/v1/postings/posting-ID.

Sample Lever data

Here's an example of the kind of response you might see with a query like the one above.

{
  "data": {
    "id": "f2f01e16-27f8-4711-a728-7d49499795a0",
    "text": "Infrastructure Engineer",
    "createdAt": 1407779365624,
    "updatedAt": 1407779365624,
    "user": "ec1cb1bb-8b58-4834-bc6e-b2af06296e4a",
    "owner": "ec1cb1bb-8b58-4834-bc6e-b2af06296e4a",
    "hiringManager": "ec1cb1bb-8b58-4834-bc6e-b2af06296e4a",
    "confidentiality": "non-confidential",
    "categories": {
      "team": "Platform",
      "department": "Engineering",
      "location": "San Francisco",
      "commitment": "Full-time",
      "level": "Senior"
    },
    "content": {
      "description": "The Infrastructure Engineer will act as...\\nSuperman.",
      "descriptionHtml": "
The Infrastructure Engineer will act as...
Superman.
", "lists": [ { "text": "Job requirements", "content": "
  • Quick learner
  • Ambitious
  • " } ], "closing": "Our company is proud to be an equal opportunity workplace.", "closingHtml": "
    Our company is proud to be an equal opportunity workplace.
    " }, "tags": [], "state": "published", "distributionChannels": [ "internal", "public" ], "reqCode": "7381912", "requisitionCodes": [ "7381912" ], "urls": { "list": "https://jobs.lever.co/example", "show": "https://jobs.lever.co/example/f2f01e16-27f8-4711-a728-7d49499795a0/", "apply": "https://jobs.lever.co/example/f2f01e16-27f8-4711-a728-7d49499795a0/apply" } } }

    Preparing Lever data

    If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. The Lever documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

    Complicating things is the fact that the records retrieved from the source may not always be "flat" — some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

    Loading data into Metabase

    Metabase works with data in databases; you can't use it as a front end for a SaaS application without replicating the data to a data warehouse first. Out of the box Metabase supports 15 database sources, and you can download 10 additional third-party database drivers, or write your own. Once you specify the source, you must specify a host name and port, database name, and username and password to get access to the data.

    Using data in Metabase

    Metabase supports three kinds of queries: simple, custom, and SQL. Users create simple queries entirely through a visual drag-and-drop interface. Custom queries use a notebook-style editor that lets users select, filter, summarize, and otherwise customize the presentation of the data. The SQL editor lets users type or paste in SQL queries.

    Keeping Lever data up to date

    At this point you've coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

    The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Lever's API results include fields like createdAt and updatedAt that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've taken new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

    From Lever to your data warehouse: An easier solution

    As mentioned earlier, the best practice for analyzing Lever data in Metabase is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Lever to Redshift, Lever to BigQuery, Lever to Azure Synapse Analytics, Lever to PostgreSQL, Lever to Panoply, and Lever to Snowflake.

    Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Lever with Metabase. With just a few clicks, Stitch starts extracting your Lever data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Metabase.