Team Delivery KPIs — Jira to Power BI

Operations report for four engineering teams built from their real public Jira tickets (Apache Hive, Cassandra, HBase, Artemis). Maps each team's own workflow onto shared stages and replays every number from the full change history.

Preview

Highlights

Artemis is fastest but one engineer delivered 46% of its tickets, a key-person risk the report surfaces
Built and validated as code; not yet opened in Power BI Desktop

Problem

Four teams with different Jira workflows needed one view of throughput, lead time and SLA, plus who carries the open work, without trusting numbers that could not be traced back to a ticket.

Approach

Jira REST extraction with full changelogs; statuses mapped to shared stages; every pulled issue counted or excluded with a reason
Beginner to intermediate: KPI cards, tickets in vs out (running totals), SLA Scale what-if slider, field parameters
Advanced: small multiples per team, bookmarks to switch chart and table, saved DAX queries with tests, calculation group
Expert: DAX user-defined functions, calendar time intelligence, visual calculations, object-level security, CI checks

Stack

Power BIDAXTMDLPBIRJira REST APIDuckDBPython

Outcome

Every team takes in more than it closes: the backlog grew by 1,645 tickets, 1,021 of them older than 180 days
Only 53% of P1 tickets met their 7-day target; 85% of open time is spent waiting in the backlog
Model: 37 measures, 8 functions; 7 report pages, 135 visuals; both validators pass