Mark Marquez
Systems Engineer with years across IT, cybersecurity, SaaS operations, and emerging AI tooling. I've spent that time in the weeds — supporting real users, hardening real networks, and shipping automation that gave real hours back. What I do best is sit between teams, find the real problem, and build the solution — the systems integration, the automation, or the data dashboard that makes the whole team more productive.
In practice that means being the bridge between Customer Success, Engineering, and Product — working closely with teams and customers to capture the domain knowledge that turns a vague problem into a high-value solution.
What I bring to a team.
Troubleshooting, Diagnostics & Documentation
Diagnose vague problems fast — logs, curl, DevTools, Wireshark, SQL debug. Reproduce, isolate, root-cause, then document — the underlying documentation that scales a team and saves the next engineer the same dig.
Data Engineering & Data Dashboards
Data dashboards built with Python and SQL that provide data-driven insights — a warehouse, tested transforms, an analytical surface for what the data means and an observability surface for whether the pipeline is healthy, two audiences never crossed.
Systems Integrations & Automation
Systems integrations and automation that replace manual work and reporting — REST APIs, webhooks, and SSO wiring services together, with Python, PowerShell, Bash, SQL, and Terraform behind them. Measurable hour-reductions on real workflows.
Systems & Cloud
Windows (10/11/Server), Linux, Microsoft 365 (Graph API), Azure, AWS. I provision and automate Active Directory, manage identity in Entra ID, codify infrastructure with Terraform, keep it observable with CloudWatch, and version everything in Git/GitHub.
Networking & Cybersecurity
TCP/IP, DNS, routers, firewalls — diagnose at the wire. Cybersecurity hardening with SIEM, EDR, MFA, IAM, and threat intelligence, plus virtualization and backups so issues don't become incidents.
AI Agents & Workflows
Agentic AI applied to real engineering work — prompt engineering and graph-based agent orchestration behind production LLM assistants, with RAG for grounded answers. Deployed where teams already work, including chat surfaces, with guardrails like confirmation gates and audit logging.
Project Portfolio
Field logs from years building real IT, data, and AI systems — each linked out to the full write-up.
