Oracle Netsuite
Senior UX designer
Oracle NetSuite is a leading cloud platform that helps businesses streamline operations and drive growth. As part of the Foundation team, a vertical layer supporting multiple application teams, I contribute to shaping the platform that powers NetSuite’s ecosystem.
As part of the Design System’s Template team, I design scalable templates that simplify interactions, ensure consistency, and connect the needs of end users and internal teams.
Beyond design, I create comprehensive design guidelines that serve as a source of truth for design teams and a practical implementation guide for developers.
A key part of my role is presenting and pitching outcomes to leadership, aligning design solutions with broader product strategy. The complexity of my work involves addressing different needs: Defining and implementing solutions, exploring and validating ideas within the product context, and conducting deeper, often AI-focused analyses to inform our future direction.
The process behind designing systems at scale.
The assigned task
As a senior designer, I frequently embrace tasks that begin with high ambiguity. The first conversation with the PM is essential; together, we clarify the problem space, align expectations, and frame the task from both technical and user perspectives.
For instance, when redesigning the "Add Multiple" pattern, the flow used across our platform for adding many items into a single list (e.g., lines to an invoice, or users to a group). I independently led the discovery process to understand existing workflows, data needs, dependencies and user behaviors, before shaping the design direction.
The scope of assigned tasks often begins just with the ticket title. (Proprietary data is blurred for compliance)
The problem space
I begin by defining key questions and partnering directly with Engineering to secure accurate, reliable information and technical constraints. Then, I conduct a comprehensive system audit, mapping inconsistencies across existing patterns, for instance, documenting nine different versions of the "Add Multiple" interaction, to expose variances and determine which core behavior should be standardized.
To ensure a truly comprehensive perspective, I collaborate closely with UX Research and Data Science to uncover both quantitative and qualitative user pain points and behaviors. The final, crucial step in problem definition is stakeholder validation: I consult with at least five different product teams who rely on this pattern to confirm real use cases, validate assumptions, and fill any remaining implementation gaps before transitioning into solution design.
Example of produced boards during the problem definition phase. (Proprietary data is blurred for compliance)
The solution and strategic pitch
The design is finalized when it effectively solves the core user problems and measurably enables our teams to deliver for end-users, balancing quality with business deadlines. Before advocacy, the solution is rigorously "bulletproofed" with PMs and stakeholders to align on implementation.
Then I prepare a high-impact presentation and a concise video pitch (under five minutes), for our VP-level leadership. The pitch is structured strategically: I begin with a data-backed explanation of the status quo and the problem solved, then walk through the solution, explaining key UX rationale and design decisions. For example, for "Add Multiple," I detail why we required changes to the search engine model (e.g., to support autocomplete and typo tolerance). In my pitch I ensures to address key leadership questions before they are asked, to gain their support and speed up decision-making.
Proprietary data is blurred for compliance.
The documentation
Once leadership approves, the design work is done, and the ticket is closed. The Documentation and Handoff phase begins immediately.
I structure documentation strictly by design system guidelines. Serving as the Source of Truth for both design teams and engineers, I carefully detail all template interactions and states. I finalize with a formal handoff meeting to ensure a smooth transition, which makes implementation faster and smoother.
Example of some documentation pages for the "Add Multiple" pattern. (Proprietary data is blurred for compliance)
The process behind idea validation and prioritization.
The idea under validation
When new initiatives comes from leadership or emerge as critical assumptions, we immediately engage in rigorous validation. This is particularly critical when dealing with high stakes projects, for example, when I was requested to validate "AI text enhance" across all touchpoints where users add or edit text.
Before introducing any new pattern into our large scale design system, we must proactively validate the idea's viability. This crucial first step ensures we minimize risk, prevent pattern redundancy, and confirm the new component will deliver high value and seamless integration across the ecosystem.
Proprietary data is blurred for compliance.
The deep analysis
Validating an idea requires careful and deep analysis, beginning with defining the problem. We first determine if a gap exists and then analyze its impact on the user experience. This involves analyzing user friction: Is this a small problem, a system block, or something users don't even notice?
This analysis then shifts to strategic planning and where to place the solution. I analyze how the pattern would work in different contexts (small vs large spaces), prioritize where we can dismiss the feature to avoid clutter. I also look ahead at how it would work with other future AI tools and what we need to do to earn and keep user trust. Finally, we decide on the best technical and Design System approach: Can we use something we already have, make an existing one better, or do we need to build a new part?
Example of produced boards during the analysis phase. (Proprietary data is blurred for compliance)
To adopt or not to adopt?
All created material, including final solutions, boards, and prototypes, is treated as a tool for exploration and learning, not a commitment to implementation. It is acceptable if the solution is discarded, as this cycle is meant for testing complex and ambitious ideas.
If analysis suggests the idea is not viable for adoption, I still present the findings. I clearly recommend pattern retirement, backing this advice with data that highlights potential problems and risks. If the solution looks good, the stage quickly moves into planning the final design for Design System integration. Even then, we may suggest further iteration. The process ends with a strong presentation and a five minutes video for leadership, ensuring the final choice is fully informed and supports our business plan.
Example of produced boards during the problem definition phase. (Proprietary data is blurred for compliance)
The deep analysis
With the fast changes in AI, I spend time studying new AI tools to give us a clear direction for our product. I deeply analyze existing solutions to see how well they solve user problems and find any major flaws in the experience or interactions.
I not only examine these external patterns but also perform a thorough analysis of our internal platform architecture and existing Design System components to determine the most effective and seamless integration points for potential AI enhancements. This work ensures our future AI adoption is informed, efficient, and strategically aligned with our platform's capabilities.