Developer Experience & Business Intelligence

DORA Metrics Platform

Building comprehensive business intelligence and developer experience tooling—automated metrics collection, intelligent notifications, and deployment gates

2024-2025
400 Deploys/Month Visibility
// the challenge

The Challenge

Engineering leadership had zero visibility into deployment velocity across 20 microservices and 4 environments. Critical business questions remained unanswered:

  • "How many deployments did we do this month?"
  • "What's our average lead time from commit to production?"
  • "Which Jira tickets are in the QA release right now?"
  • "Where are our deployment bottlenecks?"

Additionally, developers had poor visibility into pipeline execution:

  • Manual checking of pipeline status across multiple services
  • No proactive notifications for deployment events
  • Silent security vulnerabilities discovered weeks later
  • Premature QA deployments missing required Jira metadata

Business Impact: Leadership couldn't demonstrate engineering velocity to stakeholders, and QA teams were constantly surprised by incomplete releases

// the solution

The Solution: Multi-Layered Intelligence Platform

I designed and built a comprehensive DevEx platform consisting of three integrated components that transformed engineering operations:

01

DORA Metrics Collector

Python service correlating GitOps, Bitbucket, Jira, and ArgoCD data

02

Pipeline Reporter

Intelligent Teams notifications with rich context and smart routing

03

Deployment Gates

Automated quality checks preventing premature releases

// architecture

Architecture

DORA Metrics Collection Architecture

DORA Metrics CollectorPython Flask • K8s DeploymentCorrelates APIs • Exposes /metricsGitOps RepoArgoCD AppsDeployment configBitbucket APICommits • PipelinesLead time trackingJira APIIssues • Release VersionsMTTR metricsArgoCD APISync status • HealthDeploy frequencyPrometheusScrapes /metrics15+ DORA metricsGrafanaDORA dashboardsLead time • Frequencygit cloneREST APIREST APIREST APIscrapequery

Flow: DORA Collector (K8s deployment) clones GitOps repo and correlates Bitbucket, Jira, and ArgoCD APIs → Exposes Prometheus metrics → Grafana visualizes deployment intelligence (deployment frequency, lead time, MTTR, change failure rate)

// component 1

DORA Metrics Collector

A Python service that acts as a centralised correlation engine between disconnected systems.

Data Sources

  • •ArgoCD Apps Repo — Desired deployment state (Kustomize manifests)
  • •ArgoCD API — Actual deployment state, sync status, health
  • •Bitbucket API — Commit metadata, author, timestamp
  • •Jira API — Ticket enrichment (status, release versions, sprint)

Key Metrics Exposed

deployment_desired_statedeployment_actual_state
deployment_lead_time_secondsdeployment_age_seconds
ticket_in_environmentticket_fix_version
// component 2

Intelligent Pipeline Reporter

A 1000+ line Bash script that transforms pipeline events into rich, actionable Teams notifications with smart routing and context.

Smart Notification Routing

Platform Deployments Channel: Dev, QA, PreProd, Prod deployments

Security Channel: SAST/SCA alerts from Veracode/SourceClear

PR Notifications Channel: Feature branch deployments with PR context

QA Team Channel: QA deployment events for testing coordination

Easter Eggs & DevEx Enhancements

Because developer experience means making the boring stuff enjoyable:

Build #42:"The answer to life, the universe, and this deployment"
Build #404:"This build was not found... wait, yes it was"
Friday 5pm:"Bold."
May 4th:"May the Fourth be with this code"

Example Test Results Notification

✕
Harmony • DEV
Tests Failed
88
Passed
27
Failed
119
Total
74%
Success
Test Breakdown
API Tests 87/113 passed (4 skipped)
UI Tests 1/6 passed
Version: a5ece15-b4280
Tags: api-smoke, harmony, @ui-sanity
◉ Open in ArgoCD📊 View Test Report
// component 3

Automated Deployment Gates

Intelligent checks that prevent premature deployments and enforce quality standards.

Jira Release Version Check (QA Gate)

A Bash script that enforces Jira Release Version assignment before QA deployment, preventing incomplete releases from reaching QA.

1. Determine Current QA State: Queries ArgoCD apps repo to find currently deployed commit SHA

2. Identify New Commits: Uses git rev-list to find commits between QA and HEAD

3. Extract Jira Tickets: Regex matching on commit messages

4. Validate Release Versions: Queries Jira API to check each ticket

5. Block or Allow: Fails pipeline with actionable error message

Example Error Output
════════════════════════════════════════════════════
❌ QA DEPLOYMENT BLOCKED
════════════════════════════════════════════════════

The following 2 ticket(s) are missing Release Version/s:

  🎫 PROJ-1001
     https://company.atlassian.net/browse/PROJ-1001

  🎫 PROJ-1002
     https://company.atlassian.net/browse/PROJ-1002

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
📋 ACTION REQUIRED:
1. Open each ticket above in Jira
2. Set the 'Release Version/s' field
3. Re-run this QA deployment pipeline
// impact

Business Impact

400+

Deployments tracked per month

2-3 days

Average lead time measured

100%

Deployment visibility for leadership

4

Smart notification channels

Zero

Incomplete QA releases

Self-serve

Release visibility for all stakeholders

// highlights

Technical Highlights

  • → Built production-grade Python service with retry logic, caching, and parallel processing
  • → Integrated 4 separate APIs (Bitbucket, Jira, ArgoCD, Prometheus) into unified intelligence layer
  • → Designed 50+ custom Prometheus metrics enabling comprehensive DORA dashboards
  • → Implemented sophisticated Bash scripting (1000+ lines) for rich Teams notifications
  • → Created automated deployment gates with Jira API integration and delta detection
  • → Improved developer experience with actionable notifications and early validation

Want Similar Results?

I'd love to bring this same approach to your platform engineering challenges. Let's discuss how I can help your team.