Release Dashboard Service
An enterprise-grade engineering quality metrics platform that aggregates code quality, defect data, test performance, and accessibility data points — enabling data-driven engineering decisions
Overview
Release Dashboard Service is a full-stack internal engineering quality/productivity platform built to eliminate manual reporting/metrics gathering across a large engineering organization. The web application aggregates data from 300+ quality signals, 94 automated checks, processes 40K+ data points daily, and has been running reliably for over 1 year — using React 18, Spring Boot 3.2, Google BigQuery, and REST APIs. The platform serves multiple engineering teams by consolidating quality signals, code metrics, test performance, accessibility compliance, and incident metrics — serving 40+ engineering teams across Walmart Fulfillment.
Problems
Engineering teams spent significant time every day preparing:
- Engineering teams spent hours manually collecting quality metrics from multiple disconnected sources
- No single source of truth for code quality, test results, and performance data across all teams
- Accessibility (a11y) compliance tracking was done manually with inconsistent scoring across platforms
- Defect analysis required pulling data from Jira, Confluence, and multiple test management tools individually
- Release readiness decisions were based on gut feeling rather than objective, consolidated data signals
- Stakeholder reports in each team's own format with no standardized metrics or delivery schedule
Quality visibility was fragmented across dozens of tools, making it impossible to get a unified view of release health and engineering quality at scale.
Solutions
- A centralized quality signals API that aggregates 300+ metrics from multiple engineering tools into a single platform
- Automated real-time data pipelines pulling from Jira, GitHub, SonarQube, accessibility scanners, and CI/CD systems
- Standardized quality scorecards with configurable thresholds per team and per release cycle
- Interactive dashboards with drill-down capabilities from org-level KPIs to individual test results
- Automated email/Slack distribution of quality reports on configurable schedules
- REST API endpoints enabling other teams to consume quality signals programmatically
- Historical trend analysis with BigQuery-backed analytics for quarter-over-quarter comparison
Architecture
System Design — Quality Signals Pipeline
Tech Stack
Frontend
Backend
Data & API
Infrastructure
Key Features
Quality Metrics Dashboard
A single pane of glass for all engineering quality signals — code coverage, defect density, test pass rates, and accessibility compliance.
Accessibility Analytics
Live WCAG compliance data and historical accessibility trends across all platforms and teams.
Automated Email/Slack Alerts
Configurable notifications when quality thresholds are breached, with instant Slack alerts and detailed email reports.
API Data Sync Engine
Scheduled data collectors that pull metrics from Jira, GitHub, SonarQube, and CI/CD systems every hour.
Team Performance Scores
Composite quality scores per team with weighted metrics, historical trends, and sprint-over-sprint comparison.
Incident Dashboard
Real-time incident tracking with severity distribution, MTTR analysis, and on-call performance metrics.
DAPI Call Analytics
Downstream API call monitoring with latency percentiles, error rates, and throughput dashboards.
Data Export & Reporting
One-click export of quality data to CSV/Excel/PDF for stakeholder presentations and compliance audits.
Dynamic Configuration
Admin-configurable thresholds, team mappings, and metric weights without code deployments.
My Contributions
Built REST API Spring/Boot Quality Dashboard — full-stack, end-to-end, across 300+ quality signals from multiple sources
Developed job scheduling utility enabling REST API-based automated data refresh across all data collectors
Designed an ML-powered severity classification model achieving 94% accuracy for automated defect prioritization
Architected data pipeline with Google BigQuery — scalable analytics across 40K+ daily data points with sub-second queries
Automated Playwright end-to-end tests for critical user journeys reducing regression detection time by 80%
Implemented REST API security layer with OAuth2 + JWT token validation and role-based access control
Built custom caching layer reducing API response times from 3s to 200ms for high-traffic dashboard endpoints
Integrated Kubernetes health checks and auto-scaling policies handling 10x traffic spikes during release windows
Modernized SQL Server DB2 to PostgreSQL with zero-downtime migration strategy
Delivered ReSTful API documentation via Swagger/OpenAPI enabling 5 downstream teams to self-integrate
Engineering Challenges
Cross-Database Query Optimization
The platform aggregates data from BigQuery, PostgreSQL, and external REST APIs. Achieving sub-second query responses required implementing materialized views, smart caching with Redis, and pre-computed aggregation tables that refresh on configurable schedules.
Recursive Org Hierarchy Resolution
Providing roll-up metrics at the VP, Director, and Manager levels required resolving recursive organizational hierarchies from HR data. Built a graph-based hierarchy resolver that pre-computes roll-up paths and invalidates on org-change events.
JDK to SQL Migration with Java Dremtiles
Migrated from JDK 11 to 21, altering the database strategy from flat-file storage to normalized SQL. Required careful data migration, schema versioning with Flyway, and backward-compatible API contracts during the transition period.
Configurable Email/Notification Engine
Built a flexible template engine supporting dynamic content blocks, team-specific branding, and conditional sections based on metric thresholds. Thymeleaf with custom dialect extensions enables non-developer configuration of report layouts.
Multi-Source Data Aggregation
The platform aggregates from 6+ external data sources (Jira, GitHub, SonarQube, CI/CD, accessibility scanners, incident systems). Built circuit breakers, retry policies, and fallback caches to handle upstream service failures gracefully without impacting dashboard availability.
Schedule Orchestration & Reliability
Managing 50+ scheduled data collection jobs with dependencies, retries, and failure alerting. Implemented a DAG-based scheduler with dead-letter queues, ensuring no data gaps even when upstream services experience extended outages.
Lessons Learned
Design for evolving configurations, not static data
Team structures, metric definitions, and thresholds change constantly. Building a configuration-driven architecture from day one saved hundreds of hours of code changes and redeployments.
Cache aggressively, invalidate precisely
Dashboard performance improved 15x by implementing multi-layer caching — but stale data is worse than slow data. Event-driven cache invalidation with versioned keys ensures freshness without sacrificing speed.
Start with query patterns, not data models
Designing BigQuery schemas around how data would be queried (team roll-ups, time-series trends, cross-metric correlation) rather than how it's produced led to 10x simpler query logic downstream.
Contract-first API design prevents integration pain
Publishing OpenAPI specs before implementation allowed 5 downstream teams to develop in parallel. Breaking changes are caught at CI time via contract tests rather than in production.
Monitoring the monitor is non-trivial
A quality dashboard that's down or showing stale data erodes trust quickly. Built synthetic health checks that validate data freshness, not just service uptime, and alert before users notice.
Incremental delivery builds stakeholder trust
Shipping a single team's dashboard first, gathering feedback, then expanding to 40+ teams. Early adopters became advocates, smoothing organization-wide rollout significantly.