Stage 1 · Source
About
I'm a site reliability engineer with 5+ years building and automating production systems in Python, C#/.NET, and SQL Server on Azure. At One Legal (InfoTrack) I lead on-call for a court e-filing platform that law firms across California, Texas, Illinois, and New York rely on, held to 99.95% uptime.
Before that I was a software engineer at Honeywell Voice, building apps for a warehouse platform used by more than a million workers a day. My favorite work is turning manual, failure-prone processes into automation.
Stage 2 · Build
Stack
- RELIABILITY
- On-call leadership, incident response, postmortems, runbooks, KQL, Azure Monitor, Grafana
- CLOUD & AUTOMATION
- Azure (Functions, Container Apps, Key Vault, AKS), Azure DevOps, Azure CLI, PowerShell, GitHub Actions, Octopus Deploy, Jenkins, Terraform, Docker
- LANGUAGES & AI
- Python, C#, T-SQL, JavaScript, TypeScript, Bash, Claude Code, MCP
- FRAMEWORKS & DATA
- ASP.NET Web API, .NET Core, .NET MAUI, Xamarin, FastAPI, React, React Native, Entity Framework, SQL Server, PostgreSQL, CosmosDB, Oracle
Stage 3 · Test
Work, written as postmortems
Each project is told the way I'd write up an incident: what was going on, what I did, and how it ended.
PM-01Slow incident response on a deadline-driven platform
Context
An e-filing platform held to a 99.95% uptime SLA, with filing deadlines that land at midnight.
What I did
Built monitoring and alerting automation with Python, Azure Functions, and runbooks.
Outcome
Incident response time dropped from 10 minutes to 2. Missed-deadline incidents that required court declarations fell from 4–5 a week to roughly 1 a quarter.
PM-02Outstanding orders and uncollected fees
Context
Outstanding orders and uncollected fees needed to be reconciled.
What I did
Built automated reconciliation on Azure DevOps with Datadog monitoring.
Outcome
Recovered $250K+ and cut production failures by 72%, contributing to the company's highest-grossing year and first $1M revenue week.
PM-03Three-hour daily Finance reports
Context
Several Finance reporting processes each took about 3 hours a day.
What I did
Rebuilt them with multi-stage calculation layers and SQL Server bulk loading automations.
Outcome
Average run time dropped to about 5 minutes.
PM-04Slow SQL Server queries in Production
Context
Production SQL Server queries were spending too long waiting.
What I did
Analyzed waits with Database Performance Analyzer (DPA) and added targeted indexes.
Outcome
Query wait times fell by more than 60%.
Stage 4 · Deploy
Get in touch
If you have a project, a question, or a role that needs an automation person, I'd like to hear about it.
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