Dackota Johnson

Platform Engineering Lead · Mesa, AZ · Remote

I build the platforms organizations ship on — ten-plus years of GitOps delivery, Kubernetes at scale, and multi-tenant reliability. Currently leading the platform team behind Panasonic's enterprise agentic-AI platform.

# career.pipelineTrunk-based. Single artifact. Promoted through every stage.

One engineer, promoted stage to stage since 2015 — the same way I ship software. Select a stage to inspect it, or hit Promote to walk the pipeline.

freight: dackota@sha-2015 · verified in all upstream stages
✓ Synced✓ Healthy Platform Engineering Lead — Panasonic Global Applied AI (GAAI, formerly Yohana / Panasonic Well), Remote · Jun 2022 → now

Runs the company-wide Kubernetes platform every product team deploys on — ~30 engineers building KanpAI, Panasonic's enterprise agentic-AI platform: 20+ production agents across 10+ business units in the US and Japan. Leads the 4-person platform team: roadmap, priorities, mentoring, and the platform's voice to engineering leadership.

  • Delivery Replaced Spinnaker with a self-service GitOps platform on ArgoCD + Argo Workflows/Events — pipelines from one command, setup 6 hrs → 10 min, lead time 1 hr → 15 min, scaled 15 → 228 microservices
  • Promotion Drove trunk-based single-artifact promotion with Kargo across 5 teams / 16 services — weekly deploys became 15 a day, one click dev-to-prod
  • Charts Built helm-generic-app: schema-validated, secure-by-default, CI-tested — new-service packaging 3 hrs → 5 min across 16 microservices
  • Tenancy Cut tenant onboarding from 7 days of manual Vault + Terraform to minutes with a git push — bootstrap chart, External Secrets, ACK controllers
  • Reliability Zero customer-impacting incidents in GAAI's first 10 months — PodDisruptionBudgets, graceful drain, Karpenter-rolled EKS upgrades from a single Terraform PR
  • Security Multi-tenant isolation with Kyverno policy-as-code, default-deny NetworkPolicies, per-tenant gateways — then built a production-identical red-team environment in 3 hrs and fixed what it found
  • Recovery Verified end-to-end restore drills with Velero + CSI snapshots and cross-region failover; moved 30 Datadog monitors into GitOps
  • Migration Drove the platform's carve-out onto its own AWS foundation — a dozen-plus microservices moved ahead of the corporate deadline, zero data loss
  • AI tooling Shipped claude-projects: Claude Code subagents with guardrail hooks running 2–3 parallel epics — release automation, Renovate, ECR publish, a change-tracking dashboard
  • Teaching Led a 3-week on-site SRE workshop in Japan — 20 engineers from 12 Panasonic subsidiaries: SLOs, error budgets, incident response, CI/CD

# platform.metricsNumbers a dashboard would page you about — in a good way

0microservices on the delivery platform (up from 15)
0deployments per day, org-wide
0dev-to-prod lead time
0pipeline setup for a new service
0customer-impacting incidents in GAAI's first 10 months
0infrastructure cost cut migrating VMs → Kubernetes
0mean incident triage time
0run with one cluster's worth of effort
0production AI agents across 10+ business units

# kubectl.execYou have shell access. Poke around.

A resume you can query. Try help, kubectl get jobs, argocd app sync career, trace, or sudo hire dackota.

dackota@me:~ — read-only cluster, generous RBAC

↑/↓ history · clear to reset · everything is fair game

# kubectl get ns --show-labelsThe skills cluster — every namespace Running

delivery
CI/CDGitHub Actionstrunk-based devrelease-pleaseRenovateimmutable infraDORA
gitops
KargoArgoCDArgo WorkflowsArgo EventsArgo Rolloutsmulti-region promotion
cloud-iac
AWS EKSmulti-account orgTerraformTerragruntAnsiblePackerACK controllersGCP
kubernetes-platform
Helm chart designIstioTraefik / Gateway APIVaultExternal Secretscert-managerKarpenterKEDAVeleroCrossplane
security
supply-chain securitymulti-tenant isolationNetworkPoliciesKyvernoOkta SSORBACOIDC / OAuthPCI
observability-reliability
DatadogPrometheus / GrafanaNew RelicSLOs & error budgetsdisaster recoveryincident responserunbooks
languages
GoPythonBashLinuxGit
ai-tooling
Claude Code — orchestration, subagents, hooksLiteLLMinternal developer platformsself-service CLIs
leadership
team leadershiproadmap ownershipmentoring & 1:1scross-team directionplatform standardizationpartnering with eng leadership

# otel.traceYour visit, as a distributed trace

This page is instrumented like a service I'd run in production. Every section you read, every stage you click, every command you type opens a span — with real durations, attributes, and status codes. Select a span to inspect it.

trace explorer — head sampling 100%
held in memory only — no beacon, no cookie, no storage, nothing leaves your browser

# slo.dashboardThis session has an SLO. Here's how it's doing.

The same thing I taught 20 engineers in Japan, pointed at this page: an objective, an indicator measured from real spans, and an error budget that burns when the page fails you.

SLO · 99.9% of interactions complete within their latency objective measuring…
good 0 total 0 SLI error budget left 100%

Every instrumented interaction carries a latency objective — 50 ms for a pipeline stage click or a freight promotion, 100 ms for a shell command. Animations and dwell time don't count, because neither is a request. A span that misses its objective, or exits non-zero, burns budget. Go break it: rm -rf / in the shell above gets denied by policy, and a denied request is still a failed request.

Real user monitoring — your actual Core Web Vitals

LCPlargest contentful paint · good ≤ 2.5s
INPinteraction to next paint · good ≤ 200ms
CLScumulative layout shift · good ≤ 0.1
TTFBtime to first byte · good ≤ 800ms

Delivery performance — DORA, computed live from this site's own repo

Not a claim: your browser calls the GitHub API for this repository's releases and commits and does the math on the spot. Definitions are printed with the numbers, because a DORA metric without its definition is a vanity metric.

deployment frequency releases published ÷ weeks observed
lead time for changes median commit authored → first release containing it
change failure rate releases followed by a revert ÷ releases
time to restore median broken release → the release that fixed it

querying api.github.com/repos/dackota/resume-website …

# changes.feedThe platform I run when nobody is paying me

Live from changes.dackota.com — a Go service I wrote that polls my config repos, extracts tracked values, and classifies every change by impact and risk. Below is what it has actually seen: real commits, real version deltas, no screenshots. Chart and Terraform changes will show you their blast radius — what the bump did to the rendered manifests, or which resources it would replace.

of changes opened and merged by automation Renovate and release-please, unattended
changesets ingested in the window every tracked-value change, not every commit
changes per day across the platform mean across the window loaded
repositories under change tracking charts, clusters, and this site
30 days of change, colored by the highest impact that day majorminorpatchother
30 days agotoday

Pick a day to re-query the feed against that exact half-open window (since inclusive, asOf exclusive) — the same tiling the API's polling recipe uses.

impact
    querying the change feed…

    # CHANGELOG.mdReleased with conventional commits since 2015

    1. v4.0.02025

      feat!: became the platform team's lead — roadmap, priorities, mentoring, and the platform's voice to leadership. BREAKING CHANGE: carve-out from Panasonic Well onto GAAI's own AWS foundation, a dozen-plus microservices moved with zero data loss.

    2. v3.0.02022

      feat!: joined Panasonic's first forward-deployed AI engineering team. Two generations of continuous delivery shipped; the release model set here is how the rest of the org ships.

    3. v2.0.02018

      feat!: promoted to SRE. Immutable infrastructure, the VM-to-Kubernetes migration, and a taste for deleting toil that never went away.

    4. v1.0.02015

      feat: initial release — sysadmin at Infusionsoft. Three OS fleets, scripted provisioning, and the discovery that everything is better automated.