If you use Jira to manage engineering work, JQL queries can provide clear visibility into work patterns and output across different projects. These queries offer precise data about completed work, helping inform decisions about resource allocation and process improvements.

Engineering work is messy. Individual tickets tell small stories, but the big picture often stays hidden. Teams might feel productive day-to-day while technical debt silently grows. A project might seem on track until you notice one team spending most of their time fixing bugs instead of building features. These patterns emerge when you look at the data over time.

Time reveals truth in engineering. A month of data might look great - lots of completed tickets, features shipping on schedule. But zoom out to six months and you might spot concerning trends: rising bug counts, slower delivery times, growing backlogs of technical debt. A year of data shows you seasonal ebbs and flows. Two years expose deeper patterns about how your engineering organization really works.

This view of the bigger picture changes how you think about engineering effectiveness. Instead of reacting to the latest fire, you can spot smoke before it becomes a blaze. You start asking better questions: Why does our velocity drop every winter? How come this team’s bug count keeps climbing? Are our recent process changes actually helping? The answers often hide in plain sight - in your project data.

Finding patterns in your project data is surprisingly straightforward with JQL. Here’s a sample query I use to get an annual overview of our work:

Let’s break this down. We’re looking at tickets from the Mobile App project, excluding the Platform team’s work. The date range covers all of 2024 - anything that was marked as done during that year.

This query gives you every resolved ticket for 2024. From here, you can dig deeper. Want to see how many were bugs? Add AND type = Bug — Curious about feature work? Change it to type = Feature

How many bugs did the Mobile App team resolve in 2024?

How many features did the Mobile App team deliver in 2024?

Tip: You can export the output as CSV and use a spreadsheet tool to visualize the data. This makes it easier to spot trends and patterns.

The value of these queries becomes crystal clear in leadership discussions. When asked “How are the teams doing?” or “Are we improving?”, you can respond with concrete data instead of gut feelings. You can show that bug counts dropped 30% after implementing automated testing, or that the team delivered 40% more features while maintaining quality. This data backs up your decisions and helps justify future improvements.

These insights also make you more effective as a leader. Spotting a rising bug count early lets you address issues before they impact delivery. Seeing that one team handles twice the bugs of others might highlight a need for more testing resources. Having this data doesn’t just help you answer questions - it helps you drive meaningful improvements across your engineering organization.

• Jira Advanced Search Documentation
• JQL Functions Guide
• Advanced JQL Examples