Case Studies
Slide Templates
Placeholder image for title and photo layout

Michael Long · 2026

From Platform Complexity to Trusted AI Workflows

Snowflake Design Leadership Case Study

From Platform Complexity to Org Leverage

A repeatable leadership pattern I’ve used across platforms.

Make it legible

  • Map real workflows
  • Reduce cognitive load
  • Name the states and failure modes

Make it shippable

  • Clarify decision-making
  • Create async-friendly rituals
  • Turn ambiguity into small bets

Make it consistent

  • Build reusable patterns
  • Design systems + governance
  • Partner with engineering via PRs
Outcome: clearer UX · faster delivery · safer defaults · fewer surprises

Career Throughline at Platform Scale

  • Improve product clarity and team operating systems together
  • Strong fit for enterprise data complexity + AI workflow evolution, where liability and governance matter
  • Comfortable leading across product areas while staying hands-on when it counts

Nexla: Data Workflows Where Trust Matters

  • First full-time designer
  • Partnered directly with CEO + product leadership
  • Owned end-to-end UX across 50+ surfaces
  • Shipped a new 0->1 product
Nexla workflow after redesignNexla workflow before redesign

AI Inside the Workflow (Not a Side Chatbot)

  • AI proposes mappings and transforms; the user always reviews the diff
  • Preview + validation happen before any irreversible action
  • Explain "why this suggestion" and show where data will go
  • Safe defaults first; expert override when needed
Execution pattern diagram

One Nexla Experience Across Two Products

  • Unified core Nexla + Express.dev so they felt built by the same company
  • Established tokens + components + patterns for connectors, schema, and execution states
  • Documented standards and socialized them by shipping: design -> code -> PR review
  • Created a scalable feedback loop across design, product, and engineering
Operating rhythm: design spec -> implementation PR -> review -> ship -> iterate

Why Snowflake

Design leadership for legible, governable, shippable data + AI workflows.

  • Snowflake is becoming the system of record for data + AI workflows; mistakes carry real cost
  • Users need consistent patterns across surfaces (build, govern, observe, share)
  • My focus: make advanced data + AI workflow actions feel safe, obvious, and auditable; then scale that with shared patterns and systems
First 90 days: ramp on Sharing + Catalog → mature AI‑first cadence → advance agent orchestration → pilot + scale

Two proof cases behind the narrative

Why I chose these: they show power + safety in workflow design.

Bring-your-own code flows

What it demonstrates

  • Power-user capability without chaos
  • Permissions + review points
  • Clear failure modes and recovery

What I'll cover

Problem -> options -> decision -> learnings

Schema template designer

What it demonstrates

  • Make complex schema work repeatable
  • Validation that teaches, not punishes
  • Adoption through defaults and templates

What I'll cover

Workflow -> IA -> iteration -> outcomes

Format: 10-12 minutes per case · focus on decisions, tradeoffs, and how the team shipped