02 Data Architecture · Org Design · Analytics

UX Team
Skills Assessment

When two UX teams merged, leadership was staffing projects based on guesswork. I built a SQL-powered optimization engine inside Google Sheets that mapped 120 competencies across 21 designers — enabling data-driven team composition for the first time.

Role
Data Analyst,
UX Researcher, System Designer
Duration
2–3 months, survey
to full system
Tools
Google Sheets · QUERY functions
Weighted analysis formulas
Scope
21 UX professionals
120 competencies

Results

21
Team Members
Assessed across merged orgs
120
Skills Mapped
Across all UX disciplines
6
Analytical Views
Ways to slice team data
Annual Cycles
With archived historical data

The Problem

When two UX groups merged into one Enterprise UX organization, leadership ran a basic skills survey. It was surface-level and provided almost no actionable insight — so team assignments kept happening based on intuition and familiarity rather than actual capability.

Leadership couldn't identify optimal team compositions, understand individual growth areas, make strategic hiring decisions, or leverage hidden strengths sitting right in front of them.

An Unexpected Foundation

My fluency with Google Sheets' QUERY function came from an unusual place: I'd built complex spreadsheets to analyze my own Overwatch gameplay — tracking damage output, kill ratios, and survival metrics to find improvement areas.

The skills transfer was direct: multi-dimensional game stats (damage/kills/survival) mapped cleanly to multi-dimensional UX competencies (expertise/interest/role fit). Performance optimization for personal gameplay became team composition optimization for an organization.

System Architecture

Assessment Framework

  • 120 UX competencies — technical, strategic, and domain expertise
  • Expertise scale — No Experience · Working Knowledge · Mastered
  • Interest scale — Interested · Not Interested
  • Each dimension tracked separately to surface "mastered but burned out" vs "interested but developing"

Six Analytical Views

01

Individual View

Complete skill profile per team member — their full expertise and interest map across all 120 competencies.

02

Skill Analysis

How all teammates ranked on any specific skill — instantly surfacing who to pull for a given need.

03

Role Definition

Weighted skill matching against role archetypes — who's actually a researcher vs. who thinks they are.

04

Service Capability

Team capacity across four core service offerings — where are we strong, where are we thin?

05

Team Builder

Slot in any combination of teammates, instantly see aggregated strengths, gaps, and role distribution.

06

Project-Specific

Custom skill matching beyond traditional role definitions — built when leadership requested it mid-project.

The Raw Material

Everything started here — 120 skills, 21 participants, hundreds of rows. Useful for storage, useless for decisions. The QUERY engine turned this into something leadership could actually act on.

Raw Data — Survey Input
Raw skills survey data in Google Sheets showing participant responses across 120 skills

Raw survey output: participant IDs, skill labels, expertise levels, and interest ratings — all queryable but requiring a system to make sense of at scale.

Individual Teammate View — Participant 06
Individual teammate view in Google Sheets showing service offerings, roles, and skill breakdown

Automated QUERY output per teammate: service offering interest levels, role fit scores, and full skill breakdown sorted by mastery — leadership could instantly profile any team member.

The Mobile Vision

The spreadsheet solved the immediate problem. The Figma explorations showed what this system could become — a purpose-built tool for team leads to build and compare project teams on the fly.

Teammate Detail View
Mobile view showing individual teammate skills, services and role scores

Individual profile: service capacity scores, role fit rankings, full skill list with expertise level and interest flags.

Skill Drill-Down
Mobile skill view showing all teammates ranked by skill level with interest indicators

Any skill, instantly ranked — who's mastered it, who's interested, who has it but doesn't want to use it. Surfaces the nuance the raw data hid.

Team Comparison — Filter Controls
Team comparison filter interface for sorting skills between two teams

Build two teams, choose a skill to sort by — instantly see where each team is strong or exposed.

Team Comparison — Results
Team comparison results showing skill scores side by side for two teams

Side-by-side team skill scores with visual weight bars — data-driven staffing decisions in seconds, not days.

The Team Builder

Leadership could slot in any number of teammates and instantly see aggregated skill strengths, capability gaps, role distribution, and service delivery capacity. A weighted ranking system automatically scored fit — high matches received positive weights, gaps were flagged as risks.

The system operated autonomously — leadership navigated it independently without needing to understand a single QUERY formula. That accessibility was a core design requirement, not an afterthought.

What I Grappled With

The Dunning-Kruger Problem

Self-assessment data has a known distortion: team members with limited expertise often overrate themselves, while highly skilled individuals — aware of how deep their knowledge goes — rate themselves more conservatively. True experts appeared less capable than novices on paper.

I didn't implement peer validation in this version, but recognizing the pattern meant I could brief leadership on how to weight and contextualize results — adding managerial judgment as a calibration layer.

Insights Uncovered

Hidden Technical Depth

Several team members had stronger development backgrounds than leadership realized — opening new possibilities for technical UX work no one had assigned them to.

Training Investment Clarity

Assessment gaps directly informed which NN/g courses to prioritize for the team — turning abstract skill gaps into a concrete training roadmap.

What I'd Do Differently

  • Manager + employee dual assessment — both complete independently, then compare the gap as a career development conversation starter
  • Automated data refresh — integrate with HR systems rather than relying on bi-annual manual surveys
  • Peer validation layer — calibration exercises to normalize how team members interpret skill level definitions

The unrealized potential: using this for career progression conversations where you and your manager each assess you separately, then meet to discuss the differences. Never built it. Next time.

Demonstrated

Advanced Spreadsheet Engineering Data Architecture SQL / QUERY Functions System Design Strategic HR Analytics Weighted Algorithm Design Multi-dimensional Analysis Org Problem-Solving Stakeholder Research Self-Directed Learning