Platform Engineering

I Build and Operate Automation Systems

My focus is not just "writing tests" — it's building automation platforms that scale with engineering teams. That includes cloud infrastructure (IaC), CI/CD pipelines, telemetry/observability, performance budgets, security gates, and the QA automation layer that proves it works.

I prefer building systems that are easy to operate: clear ownership, clear runbooks, and metrics that make quality measurable.

Capabilities

Six Pillars

CI/CD Automation Gates

Multi-layer pipelines: lint → typecheck → unit → integration → E2E. Fast feedback via parallelization and smart retries (flake-aware). Every merge is gated.

  • Parallel test execution
  • Flake-aware retry logic
  • Artifact publishing
  • Automated rollback

Infrastructure as Code

Terraform modules with least-privilege IAM. GitHub OIDC federation — no long-lived keys. Cost-aware defaults and environment promotion gates.

  • Terraform + HCL
  • GitHub OIDC (no static keys)
  • Multi-environment promotion
  • Cost guardrails

Test Observability

Pass-rate and flake-rate trend tracking across repos. Quarantine workflow for flaky tests. Telemetry-first mindset — if you can't measure it, you can't improve it.

  • Flake rate tracking
  • Test quarantine workflow
  • Coverage trend analysis
  • Quality telemetry dashboard

Performance Budgets

Lighthouse CI budgets enforced in pipeline. P95/P99 thinking: define acceptable latency thresholds and enforce them before every deploy.

  • Lighthouse CI integration
  • P95/P99 latency budgets
  • Bundle size monitoring
  • Core Web Vitals tracking

Security Automation

OWASP-style scanning, dependency hygiene, secrets detection. Secure-by-default pipelines that fail on critical findings — not optional manual reviews.

  • OWASP dependency scanning
  • Secrets detection (pre-commit)
  • WAF + rate limiting
  • Least-privilege IAM

Operations & Maintainability

Runbooks, checklists, clear ownership. Environment drift management. Design for maintainability — the next person should be able to operate it.

  • Runbooks for every system
  • Incident triage playbooks
  • Environment drift detection
  • Documented architecture decisions
Architecture

Reference Pipeline

PR / Commit
  └─► CI Pipeline (lint / typecheck / unit)
        └─► Integration tests (DB / services)
              └─► E2E + a11y + visual
                    └─► Perf budgets (Lighthouse / load)
                          └─► Security gates (deps / secrets / ZAP)
                                └─► Publish artifacts (reports / screenshots)
                                      └─► Telemetry snapshot + dashboard
                                            └─► Alerts / triage playbooks
Reliability

SLOs & Operational Targets

This portfolio is intentionally operated like a production system. These are the same signals senior cloud/platform teams look for: SLOs, SLIs, error budgets, and a repeatable incident drill loop.

99.9%

Dashboard Availability

Synthetic HTTP checks + uptime monitoring

Monthly

< 24h

Telemetry Freshness

Time since last metrics update

Rolling

99.9%

AWS Proxy Reliability

Lambda errors + API Gateway 4xx/5xx rates

Monthly

< 500ms

P95 Response Time

API Gateway + Lambda duration percentiles

Rolling

Pattern: measure → alert → drill → postmortem → fix. High-comp cloud roles are hired to hit SLOs under cost and security constraints.

Security

Receipts, Not Buzzwords

Every security claim has evidence behind it. WAF configs, IAM policies, attack simulations, and threat models — designed for cloud/infrastructure reviewers and senior engineers.

WAF + Rate Limiting

CloudFront-scope Web ACL with rate-based rules. API Gateway stage throttling. Attack simulation script proves the controls work.

Evidence: waf-rate-limit.txt, attack simulation script, Terraform CloudFront+WAF module

IAM Least Privilege

Lambda has only s3:GetObject for a single key. DynamoDB operations limited to specific table and actions. No wildcard policies.

Evidence: IAM policy JSON, GitHub OIDC trust policy

Token Strategy

x-metrics-token shared secret for API auth. No long-lived AWS keys anywhere — GitHub OIDC federation for all CI/CD to AWS interactions.

Evidence: OIDC trust policy, token validation middleware

Threat Model

Documented abuse cases with mitigations: API scraping (token + rate limit), secrets exposure (server-only), untrusted artifact input (schema-validated), blast radius containment.

Pattern: least privilege + untrusted input handling + safe degradation

Operations

Incident Drills

Every failure mode has been tested. These aren't theoretical — each drill was executed and the response validated.

ScenarioResponseStatus
GitHub API rate limits exceeded
Fall back to Snapshot mode (committed metrics.json)Tested
Missing CI artifact
Scan back through recent runs, degrade to SnapshotTested
AWS proxy token mismatch
CloudWatch alarm fires, auto-degrade to SnapshotTested
S3 object missing
Fail closed (no secrets leak), degrade gracefullyTested

See It Running

Check the live dashboard or download the operational artifacts.

Live DashboardArtifacts & EvidenceHire Me