Jira Loader

The Jira loader pulls project management data from your Jira Cloud or Jira Data Center instance into your data warehouse. It uses Jira’s REST API and supports incremental sync via the updated timestamp on issues.

Prerequisites

• A Jira Cloud or Jira Data Center instance
• A Jira user with access to the projects you want to sync
• A connected Warehouse (target warehouse) with write permissions on the target schema

Authentication

The Jira loader supports two authentication methods.

OAuth 2.0 (Jira Cloud)

1. In SignalSmith, click Add Loader and select Jira
2. Choose OAuth 2.0 as the authentication method
3. Click Connect with Jira
4. You’ll be redirected to Atlassian’s authorization page
5. Select the Jira site you want to connect
6. Grant SignalSmith access and click Accept
7. You’ll be redirected back to SignalSmith with the connection established

SignalSmith requests the following scopes:

API Token (Jira Cloud or Data Center)

For environments where OAuth isn’t practical:

Jira Cloud:

1. Go to Atlassian API Token Management
2. Click Create API Token
3. Label the token (e.g., “SignalSmith Loader”)
4. Copy the generated token
5. In SignalSmith, provide:

Jira Data Center:

1. In Jira, go to your Profile > Personal Access Tokens
2. Click Create Token
3. Name the token and set an expiry (or no expiry)
4. Copy the generated token
5. In SignalSmith, provide:

Available Objects

Issues and Custom Fields

Jira issues are the primary data object. Each issue includes:

• Standard fields: summary, description, status, assignee, reporter, priority, issue type, created/updated dates
• Custom fields: Any custom field defined in your Jira instance is extracted as an additional column

Custom fields are named using their field ID (e.g., customfield_10001). The issue_types and field metadata tables provide human-readable labels for mapping.

Issue Changelog

The issue changelog captures every field change on an issue — status transitions, assignee changes, priority updates, sprint changes, and more. Each changelog entry includes:

• field — The field that changed
• from_value / to_value — The previous and new values
• author — Who made the change
• created — When the change occurred

This data is essential for building cycle time, lead time, and flow metrics.

Sprints

Sprints are extracted from Jira Software boards. Each sprint includes:

• Sprint name, start date, end date, and completion date
• State (future, active, closed)
• Board association

Sprint-to-issue assignments are tracked via the sprint custom field on issues.

Configuration

JQL Filter

You can optionally provide a JQL (Jira Query Language) expression to limit which issues are synced. This is useful for large Jira instances where you only need an access filter of projects or issue types.

Examples:

project IN (ENG, PLATFORM) AND issuetype IN (Bug, Story, Task)
created >= -365d
project = ENG AND status != Done

Scheduling Notes

• Rate limits: Jira Cloud enforces rate limits per user. SignalSmith handles rate limiting with automatic backoff. For large instances with many issues, the initial backfill may take several runs to complete.
• Pagination: Jira’s REST API returns a maximum of 100 results per page (50 for search). SignalSmith handles pagination automatically.
• Custom field performance: Instances with many custom fields may have slower API responses. Consider limiting the custom fields extracted if you don’t need all of them.
• Changelog volume: The issue changelog can be very high-volume for actively worked issues. Consider disabling changelog extraction if you don’t need flow metrics.
• Jira Data Center: Data Center instances may have different API behavior and rate limits compared to Jira Cloud. Ensure your Data Center version is 8.x or later for full API compatibility.

Schema Mapping

Jira field types are mapped to warehouse-compatible types:

Troubleshooting

Next Steps

• Create a model to transform your raw Jira data
• Build engineering metrics dashboards (cycle time, throughput, sprint velocity)
• Correlate engineering activity with customer outcomes using other loader data