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The problem
Teams often get over a thousand alerts a day. Real incidents get buried, on-call engineers burn out, and nobody trusts the dashboards.
What I do
Audit existing alerts and find the noisy, duplicate and non-actionable ones
Rebuild alerting around SLOs (availability, latency, error rate)
Design one clear dashboard: key metrics, latency percentiles, error budget, and active problems with root cause
Sample result shown in the video (demo data)
Alerts per day: 1,284 → 12 actionable
Availability: 97.8% → 99.95%
p95 latency: 842 ms → 187 ms
Error rate: 4.8% → 0.21%
Good fit if you
Run a production service and feel alert fatigue
Use Dynatrace or a similar APM tool and want better dashboards
Need a clear fix plan, not just a report
Message me with your stack and your biggest alerting pain, and I’ll reply with a quick plan.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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nice one dude
Your best-selling dish could be losing you money.
That insight shaped my dashboard work on DineIQ, a restaurant analytics competition project.
My challenge: turn complex analytics into clear, useful views. I approached it with focused Django dashboards, practical filters, and role-based access—helping managers explore their location’s menu performance and wastage together.
My role covered full-stack integration, UI/UX, dashboards, and architecture. Built with synthetic competition data, DineIQ illustrates why sales volume alone doesn’t tell the full story.
Better decisions start with better visibility.
#FullStackDevelopment #Django #DataAnalytics
Amazing work