Featured work
Three projects that reflect how I work
These case studies were selected because they show the kind of engineering I do most often: building shared systems, clarifying product complexity, and creating tooling that helps teams move faster.
Internal developer tooling
KBRA AI Tooling CLI
An internal CLI that turned shared AI workflow assets from informal file-sharing into a governed, low-friction distribution model.
Context
As KBRA pushed for broader participation in internal AI tooling, I built and shipped a CLI in February 2026 that packaged and distributed shared skills and related workflow assets across technology.
Problem
Useful skills and workflow customizations existed, but sharing was inconsistent and fragile. Teams relied on direct messages, copied files, ad hoc repo references, and person-to-person knowledge transfer, which made reuse hard and updates unreliable.
Approach / key decisions
- Modeled the workflow as a package-manager-style experience instead of a shared repository plus docs, because the missing layer was installation and discovery in local environments.
- Optimized v1 for casual users with `npx`, guided prompts, and a choice between global and project-level install targets, while still exposing flags for advanced users who wanted to skip prompts.
- Published installable assets with the npm package source and ran them through working-group review before release, so contribution stayed open enough to grow while installs stayed trusted and versioned.
- Added explicit `update` and `remove` flows later as the product matured, after initially prioritizing broad availability and simple install behavior.
Outcome
- Standardized distribution for shared skills and related AI workflow assets instead of relying on informal person-to-person sharing.
- Created a governed contribution path that grew from a single maintainer into regular contributions from multiple engineers across the organization.
- Supported a broader push toward deeper AI-tooling use by making shared assets easier to discover, install, and reuse.
Scope and evidence
- Released to KBRA Technology in February 2026
- Regular contributions from 5 engineers across the org
- Goal: every technologist contributes something by end of 2026
Stack / scope notes
Platform architecture and delivery
KBRA Financial Intelligence
Platform ownership across KFI's application architecture, delivery model, and search/data systems that made a complex product easier to build and ship.
Context
KFI is a financial-intelligence platform used for benchmarking, counterparty-risk analysis, portfolio monitoring, and banking-relationship analysis across more than 10,000 U.S. banks and credit unions, with scope spanning web, APIs, search, and supporting delivery systems.
Problem
The product needed to keep shipping while an immature application and delivery platform was creating release friction, duplicated patterns, slow developer workflows, and a higher cost to build new product capabilities.
Approach / key decisions
- Contributed to modernizing the application foundation from an aging Angular plus custom React adapter setup to Next.js, while helping move GraphQL from a legacy PHP implementation to Apollo.
- Helped push the team from code freezes and long-running branches toward trunk-based development, feature flags, and a faster release model where every push to `main` builds through UAT.
- Built key parts of the search and data platform for company and bank screening, including index definitions, custom tokenizers, ingestion pipelines, CI/CD, and the API layer.
- Designed patterns and modules that were later reused in the M&A screener, turning the initial screening work into reusable platform capability instead of a one-off feature.
Outcome
- Reduced release time from a full-day, coordination-heavy event to about five minutes by a single developer.
- Lowered the cost of building new capabilities by improving the application and API foundation underneath the product.
- Enabled richer product behavior, including reusable screening infrastructure and newer capabilities like portfolio report building across multiple data sources.
Scope and evidence
- 10,000+ banks and credit unions in product scope
- 40M+ records loaded during off-hours
- ~1 full day to ~5 minutes release-time reduction
Stack / scope notes
Shared UI systems
Design System Infrastructure
A token-first design system that treated shared UI decisions as delivery infrastructure rather than visual polish.
Context
Across KBRA products, and even within KFI itself, I worked on shared tokens, reusable primitives, and the operating model needed to turn repeated UI decisions into durable infrastructure.
Problem
Frontend teams were repeatedly solving the same interface problems in slightly different ways. Developers often copied values directly from Figma into code, inconsistently, which increased implementation cost and made accessibility, usability, and maintenance harder to sustain.
Approach / key decisions
- Led the engineering side of a token-first shared system, using Style Dictionary with JSON token sources that could build into CSS, JavaScript, Tailwind CSS, and other targets.
- Used tokens as the adoption wedge because they were lower-friction and JavaScript-agnostic, then layered reusable primitives on top to standardize behavior, accessibility, and recurring implementation patterns.
- Scheduled weekly mobbing sessions with design and volunteer engineers to codify repeated decisions, produce shared artifacts, and improve how both teams worked together.
- Treated the system as delivery infrastructure, not a styling layer, so teams could stop rebuilding common UI logic and translate less design intent from scratch.
Outcome
- Roughly halved average frontend complexity across the organization by reducing one-off design decisions and bespoke implementation work.
- Created a more reusable, accessible base for frontend delivery without forcing immediate convergence on one component library.
- Improved design-engineering collaboration by replacing ad hoc Figma-to-code translation with codified shared decisions.
Scope and evidence
- Style Dictionary pipeline with multi-target outputs
- Roughly halved average frontend complexity across the organization
- Cross-team collaboration spanning design, product, and engineering