Shipping Halliburton's first unified data platform through an AI-native design-to-code pipeline.
The first tool at Halliburton where engineers can find, trust, and access any data asset in minutes.
01 — Problem & Context
Problem
Data assets live across disconnected systems with inconsistent metadata and unclear ownership.
Goal
A unified marketplace for curated data, reports, models, and AI agents — part of Halliburton's Data Ribbon.
Success
Engineers find, validate, and request access to any data asset in under a few minutes.
02 — Process
1.0
Discovery
Aligned with the Technology Director and AI lead on vision, then split the work into two phases — MVP and Trust/AI — so the design system could scale without rework.
2 strategies · 1 foundation2.0
Research
Mapped 4 persona groups, 5 user flows, user journey maps, and the information architecture that later powered the MCP hand-off.
4 personas · 5 flows · IA3.0
Prototype & Design System
Started with AI-generated prototypes to validate structure fast, then built a scalable design system with reusable tokens and components.
AI prototype · Figma system4.0
Validation
Two rounds of testing with real users — Data POD Leads and Product Owners. Caught blind spots before a single line of code was written.
2 rounds · 10 users5.0
Hand-off to Dev
Connected dev team to Figma through an MCP server. Developers pulled specs, tokens, and components directly into their IDE — no screenshots, no outdated docs.
Figma MCP · live specs6.0
Post-launch Research
Tracked adoption, task success, and friction points after launch. The platform keeps evolving based on real usage data.
Analytics · iteration03 — UX Strategy
Phase 1 built the foundation — one marketplace to find, trust, and access any data asset. Structure and core workflows first; AI and social came later.
| Vision | Provide a user-friendly means of accessing curated, trusted data, reports, models, and AI Agents that are a part of Halliburton's Data Ribbon. |
| Problem | Users cannot efficiently work with data because it is difficult to find, difficult to access, and not easy to compare across multiple sources. |
| Success looks like | Data engineers use the dashboard daily as the primary entry point to find, validate, request access, and consume data assets within minutes — not days or weeks. |
Data Scientist / Engineer
Browse data products, fast and relevant search, preview data, request & obtain access quickly.
Business User
Find existing reports across BI tools that answer questions from trusted data.
Data Product Owner
Manage data product info, approve/reject accesses, define level of access.
Data POD Lead
Oversee data products across teams, monitor quality and access patterns.
What I delivered end-to-end on this project:
Phase 2 turns browsing into AI-powered discovery. Users describe what they need in plain language and get ranked, trustworthy results.
| Vision | Transform the Data Marketplace from a browsing tool into an intelligent, trust-driven data discovery platform powered by AI. |
| Problem | Users can find data but cannot judge if it's reliable, relevant, or safe to use. Discovery is manual, trust signals are absent, and social knowledge is invisible. |
| Success looks like | Engineers and business users describe what they need in plain language, receive ranked trusted results in seconds, and make access decisions confidently. |
| Phase 1 → 2 | The existing browse experience moves to a secondary role. A new AI-powered entry point takes its place — built around intent rather than navigation. |
04 — Research
4 persona groups, user flows, and end-to-end journey maps shaped every design decision.
Users
Creators & Approvers
Data Scientist / Engineer
| Who | Technical specialist working with Python, ML models, and data integrations. |
| Pain | Hard to find datasets across sources; long approval cycles; unclear ownership. |
Business User
| Who | Analysts, PMs, and business stakeholders using BI tools. |
| Pain | Too much raw data, too few curated business-ready reports; hard to trust freshness. |
Data Product Owner
| Who | Product manager responsible for governance and lifecycle of data products. |
| Pain | Fragmented ownership; too many manual approval steps; slow onboarding. |
Data POD Lead
| Who | Team/Tech Lead for a data engineering POD or domain. |
| Pain | Heavy manual documentation work; poor visibility into who uses their products. |
05 — Prototype & Design System
Instead of pixel-perfect mockups, I started with an AI prototype to test concepts and layouts before investing in high-fidelity design.
Once the structure was proven, I built a scalable design system — reusable components, tokens, and patterns — then the final mockups for the dev team.
AI-powered home page with natural language search and personalized recommendations
One system, two themes, zero duplication. Every color flows through three layers — primitives, semantic tokens that swap for light/dark, and component tokens.
~158 primitive · ~45 semantic · ~459 component tokens across 2 themes.
06 — Validation
Before moving to development, I ran two structured feedback sessions with the primary user groups — validating designs, aligning on roles and workflows, and catching blind spots early.
Design review with Data POD Leads — the technical owners who create and maintain data products. Validated roles, publishing workflows, and access management.
Key takeaways
Design review with Data Product Owners — the business owners responsible for final approval and publication. Validated publication flow, access governance, and role alignment.
Key takeaways
07 — Hand-off to Dev
Traditional hand-off means screenshots, redlines, and a PDF that's outdated the moment you save it. Instead, I connected developers directly to the Figma design system through MCP (Model Context Protocol) — they pulled specs, tokens, and interaction states straight into their IDE in real time.
How it flows
08 — Post-launch Research
The platform is in active use while testing continues. Rather than waiting to measure success, I designed the post-launch research program upfront — combining analytics, AI-powered tools, and continuous user feedback.
| Adoption | Active users, onboarding completion rates, and frequency of return visits across all persona groups. |
| Task success | Time-to-find for datasets, search-to-access conversion, and first-attempt success rate for access requests. |
| Satisfaction | In-product micro-surveys and follow-up interviews triggered by key user actions. |
| Pain points | Friction identified through AI-summarized session replays, support tickets, and heatmap analysis. |
| Analytics copilot | Natural language queries across usage data to spot trends and cohorts faster than manual dashboards. |
| AI session replay | LLM-generated insights from user sessions instead of watching hundreds of recordings manually. |
| Continuous interviews | Weekly short conversations with real users, built into the process rather than run as one-off studies. |
| Feedback synthesis | Combining survey responses, support tickets, and interviews into themed insights automatically via LLM synthesis. |
| Listen | Collect feedback through multiple channels — surveys, session replays, interviews, and support tickets. |
| Synthesize | Theme the findings and prioritize by impact on user tasks and business goals. |
| Ship | Iterate on the most critical friction points in fast, focused cycles. |
| Validate | Re-measure to confirm the change actually moved the metric. |
09 — Outcome
The platform is built, validated with real users, and in active use while testing continues.
Already in place: a three-tier design system, dev-integrated MCP pipeline, two-phase UX strategy, two rounds of stakeholder validation, and a Figma-to-code workflow producing components 1:1 with the design system.
10 — Reflection
What I'd do differently
Involve data governance stakeholders earlier in the process. Their requirements only became clear mid-project, which meant reworking parts of the access request flow that were already in motion.
What this project taught me
Leading a project end-to-end as the only designer taught me seniority isn't about headcount — it's about owning decisions across the whole stack. AI in design isn't magic, it's leverage: it speeds up what you already understand and is useless for what you don't. The best outcomes came when I knew exactly what I wanted before asking.
What I'm taking forward
The Figma + MCP pipeline is going into every project from here. Once you've shipped components 1:1 with the design system on the first pass, traditional hand-off feels broken.