Hi @greengil  good question, I went through this something similar recently, so sharing what I found.

My instinct was also to build it in Python, but once I dug in, the "just write a script" path hides a lot of pain:

• Deletions are invisible. Jira's REST API doesn't return deleted issues. Without webhooks, you'll have ghost records in Delta forever.
• Field history isn't free. The API gives you current state, not change history. Reporting usually needs history, which means building and maintaining it yourself.
• Archived issues aren't returned in JQL queries, only by ID.
• Rate limits, pagination, schema drift for custom fields, all real work.

Fivetran's Jira connector handles all of this natively, JQL-based incremental sync, webhook-based deletion capture, auto-populated ISSUE_FIELD_HISTORY tables, schema drift detection, MERGE into Delta, and it's available through Databricks Partner Connect for quick setup. There's also a free dbt package (fivetran/dbt_jira) with pre-built analytics models.

My take: I would suggest go with Fivetran unless you have a specific reason not to - high volume cost concerns, need for archived issues, or data residency restrictions. Custom Python makes sense for narrow use cases, but it's weeks of build plus ongoing maintenance.

References I did research and came up with solution, please take a look, I think you will find it really helpful:

• Fivetran Jira: Jira connector by Fivetran | Fivetran documentation
• Fivetran Databricks destination: Databricks database connector by Fivetran | Fivetran documentation
• dbt Jira package: fivetran/dbt_jira: Data models for Fivetran's Jira connector built using dbt.

Happy to dig in further if you're leaning one way.