My Mission
When I joined eBay, the company's internal data ecosystem was spread across multiple products, services, and engineering teams. Different user groups—from data, software, and ML engineers to applied researchers, analysts, and business users—needed reliable access to trusted data, but workflows, infrastructure, and discovery experiences were fragmented. At the same time, the rapid emergence of AI highlighted the need for a stronger data foundation, making accessibility, governance, metadata, and self-service essential prerequisites for AI-powered products and decision-making.
Over nearly three years, I partnered with product managers leading some of eBay’s most ambiguous platform initiatives, helping define product direction through strategic research, platform architecture, reusable interaction models, and AI-enabled workflows. My work focused on reducing uncertainty before engineering investment, connecting fragmented data experiences, and establishing scalable foundations across multiple enterprise platforms. The engagement culminated in a unified AI and data platform strategy that brought these initiatives into one coherent product vision and provided a clear foundation for the next stage of implementation.
Over nearly three years, I partnered with product managers leading some of eBay’s most ambiguous platform initiatives, helping define product direction through strategic research, platform architecture, reusable interaction models, and AI-enabled workflows. My work focused on reducing uncertainty before engineering investment, connecting fragmented data experiences, and establishing scalable foundations across multiple enterprise platforms. The engagement culminated in a unified AI and data platform strategy that brought these initiatives into one coherent product vision and provided a clear foundation for the next stage of implementation.
FEATURED CASE STUDIES
Customer Data PlatformOwned UX strategy and workflow definition for a customer intelligence surface, translating stakeholder goals into a structured scope and roadmap. Defined primary flows and key interaction patterns, then mentored a designer to execute high-fidelity UI, with ongoing reviews and usability testing to validate the experience.
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Unified AI & Data SuiteTransformed multiple enterprise research initiatives into a unified product strategy and experience blueprint, defining the solution information architecture, product model, AI-assisted workflows, and scalable foundations for a next-generation suite of AI & data platforms and services.
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Dynamic CloudDesigned self-service capacity provisioning and governance workflows enabling engineering and analytics teams to scale infrastructure safely. Defined platform primitives. Translated complex operational requirements into clear, guided UX patterns shared across several cloud data products.
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Data Platform • UX Research • Product Strategy • Enterprise UX • Self-Service • AI-Native Design • Information Architecture
Customer Data Platform
Extending a technical data platform into a business-facing decision experience
The Customer Data Platform was designed to help teams discover customer data, build customer data applications, create reusable business-aligned segments, form audiences, and reuse customer insights and audiences across product and marketing workflows.
Across three connected initiatives, I helped turn early and partially developed concepts into more coherent, scalable experiences. I structured end-to-end workflows, improved information architecture, introduced consistent design-system patterns, partnered with Product Management on prioritization, directed a junior UI/UX designer through detailed execution, and led interviews and usability testing.
The first two initiatives established foundational workflows: first, for engineers, who use customer data to build applications, then for an internal team creating and managing reusable business segments. The next opportunity was to determine how business users could access and interpret that data more independently.
I led discovery research with Product Marketing Managers, then transformed the findings into a concept of an AI-assisted Customer Data Platform for Business Users that balanced automation with direct control, evidence inspection, and audience comparison.
Across three connected initiatives, I helped turn early and partially developed concepts into more coherent, scalable experiences. I structured end-to-end workflows, improved information architecture, introduced consistent design-system patterns, partnered with Product Management on prioritization, directed a junior UI/UX designer through detailed execution, and led interviews and usability testing.
The first two initiatives established foundational workflows: first, for engineers, who use customer data to build applications, then for an internal team creating and managing reusable business segments. The next opportunity was to determine how business users could access and interpret that data more independently.
I led discovery research with Product Marketing Managers, then transformed the findings into a concept of an AI-assisted Customer Data Platform for Business Users that balanced automation with direct control, evidence inspection, and audience comparison.
Platform Strategy · AI Workflows · Research-to-Strategy · Information Architecture · Data Products · Human-in-the-loop · Product Vision
Unified AI & Data Suite
Defining the product strategy and experience architecture for a next-generation enterprise AI & Data platforms and services
As eBay's data ecosystem expanded, multiple platform initiatives—including data discovery, trusted data products, governance, and AI-assisted workflows—were evolving independently. While each initiative addressed important user needs, research revealed that fragmentation across products had become a primary barrier to adoption.
Rather than designing another standalone experience, this project focused on defining how these capabilities should work together as a single, coherent solution. The result was a comprehensive product strategy and experience blueprint that established the North Star vision, product architecture, information architecture, platform principles, shared object model, capability framework, and phased implementation strategy for a future Unified AI & Data Platform.
Due to confidentiality, this case study focuses on strategic thinking, design process, and product decisions rather than internal implementation details or proprietary product designs.
Rather than designing another standalone experience, this project focused on defining how these capabilities should work together as a single, coherent solution. The result was a comprehensive product strategy and experience blueprint that established the North Star vision, product architecture, information architecture, platform principles, shared object model, capability framework, and phased implementation strategy for a future Unified AI & Data Platform.
Due to confidentiality, this case study focuses on strategic thinking, design process, and product decisions rather than internal implementation details or proprietary product designs.
Cloud Platform • Platform Primitives • Service Design • Developer Experience • Automation • Workflow Optimization
Dynamic Cloud
Scaling cloud infrastructure through automation and self-service
Led UX across a multi-product internal cloud data ecosystem, replacing Jira- and Slack-based provisioning with guided self-service, autoscaling, visible lifecycle states, request tracking, governance touchpoints, and reusable platform patterns across several products.
AI-Native Design Workflow
Research as the source of truth for the entire design process
During my final projects at eBay, I began developing AI-assisted research workflows that combined thematic analysis, synthesis, dependency mapping, and rapid artifact generation. These experiments significantly accelerated research analysis and later became the foundation for a broader AI-native design process.
I explored workflows that transformed research evidence into structured design artifacts, using interview data as the primary source for product requirements, user stories, information architecture, realistic prototype content, UX copy, and evidence-based design prompts. Rather than generating placeholder content, the process grounded design decisions in users' language, mental models, and real-world scenarios, resulting in more accurate prototypes and more meaningful stakeholder discussions.
I also experimented with specialized AI agents responsible for different stages of the design process, including semantic analysis, requirement extraction, UX writing, design critique, and perspective-based review from different user and stakeholder viewpoints.
These experiments are the foundation for my vision of collaborative AI design systems, where specialized agents accelerate discovery, design, and decision-making while keeping human judgment at the center.
I explored workflows that transformed research evidence into structured design artifacts, using interview data as the primary source for product requirements, user stories, information architecture, realistic prototype content, UX copy, and evidence-based design prompts. Rather than generating placeholder content, the process grounded design decisions in users' language, mental models, and real-world scenarios, resulting in more accurate prototypes and more meaningful stakeholder discussions.
I also experimented with specialized AI agents responsible for different stages of the design process, including semantic analysis, requirement extraction, UX writing, design critique, and perspective-based review from different user and stakeholder viewpoints.
These experiments are the foundation for my vision of collaborative AI design systems, where specialized agents accelerate discovery, design, and decision-making while keeping human judgment at the center.