Journey

Semantic Versioning Automation

Before this, versioning was a manual process prone to human error and inconsistency. I built a custom automation where PR merges triggered automatic version tag generation — major, minor, or patch — based on the nature of the change, and immediately kicked off QA-ready builds across iOS, Android, and web.

The result: hands-off versioning control. Engineers merged their PRs and the pipeline handled the rest — no manual tag creation, no “what version is this?” ambiguity, no skipped QA builds.

Centralized Jenkins Endpoint

Release pipelines across environments were fragmented — triggering a deployment in one environment didn’t automatically coordinate with others. I built a centralized Jenkins API endpoint that any job could call to trigger other builds with dynamic parameters.

This created a single coordination layer for the entire release pipeline, making multi-environment deployments consistent and scriptable.

Azure APIM & Bicep

Implemented CI/CD automation for API deployments using Microsoft Azure API Management and Bicep, bringing infrastructure-as-code practices to the API deployment process and reducing the manual steps involved in promoting changes across environments.

The Shift

Until this point my work at CyraCom had been backend-focused. Taking on the mobile refresh meant stepping into unfamiliar territory — React Native, Expo’s build pipeline, platform-specific quirks across iOS and Android — while still being responsible for backend work continuing in parallel.

The Work

Expo was chosen as the all-in-one development platform: a single TypeScript codebase targeting iOS, Android, and web. I led the refresh effort technically while collaborating with the team lead to coordinate offshore developers — keeping their work aligned with delivery milestones and integration points on the main branch.

Cross-timezone coordination added a layer of process overhead that had to be managed deliberately: clear task definitions, documented interfaces, and regular sync points to catch drift before it compounded.

The Problem

CyraCom’s monolith had grown to the point where teams were stepping on each other — changes in one area of the API caused unexpected side effects elsewhere, and the lack of clear boundaries made onboarding and reasoning about the system difficult.

The Audit

I conducted a structured review of the API surface, identifying ownership boundaries, coupling points, and high-risk dependencies. The output was a prioritized roadmap: which services to extract first, what the seam points should be, and how to do it incrementally without disrupting a production system serving healthcare clients.

Documentation was a first-class deliverable — not an afterthought. The goal was a roadmap someone could hand to a new engineer and have them immediately understand why each decision was made.

The Challenge

CyraCom’s platform routes real-time interpreter calls — latency and reliability are non-negotiable. During the platform transition, the team hadn’t yet committed to a specific media server. Building call routing tightly coupled to any one vendor would have created significant rework the moment a decision was made.

The Design

An RTC Bridge as a vendor-agnostic abstraction layer: a microservice that handled WebRTC call routing through a stable interface, with the media server integration behind a swappable adapter. Whatever media server the team ultimately chose, the bridge would accommodate it without changes to the consumer’s experience.

This was an exercise in designing for decisions that hadn’t been made yet — the interface contract had to be stable even when the implementation behind it was still open.

The Problem

During the migration, high-frequency API requests were hammering the database. With clients like CommonSpirit Health and John Hopkins Medicine relying on the platform, any degradation in response times had real clinical consequences — interpreters delayed means patients waiting.

The Solution

I designed a Redis caching layer that sat in front of the most frequently hit endpoints — over 80 in total. The work involved:

• Identifying which endpoints were read-heavy and safe to cache
• Defining TTL strategies appropriate to the data freshness requirements of each endpoint
• Ensuring cache invalidation was consistent and didn’t introduce stale-data bugs
• Integrating the layer in a way that was transparent to existing consumers

The caching layer decoupled API throughput from database capacity during a period where both the monolith and the emerging microservices were hitting the same underlying data store — buying breathing room for the broader migration to proceed safely.

The Shift

Transitioning from QA to engineering meant going from understanding how systems break to being responsible for building and maintaining them. CyraCom’s platform operates in a high-stakes environment — real-time interpreter routing in clinical settings — which raised the bar on reliability and correctness from day one.

The codebase was a monolith under active pressure to evolve. That tension between stability and modernization became the defining challenge of my time there. It was also a time of great personal growth and understanding.

What I Built

The department had a bottleneck: not enough QA coverage, but budget constraints on headcount. I spotted an opportunity — the technical call support team already had the product knowledge and analytical instincts QA requires.

I designed a rotation program that pulled top performers from support into QA roles on 3-month cycles. I owned the full lifecycle: recruiting, onboarding, training, daily task prioritization, and performance development for a rotating team of 4 interns.

The Outcome

The program expanded testing capacity without adding permanent headcount, reduced costs, and created a clear growth path for support personnel who wanted to move into engineering-adjacent work. Leadership overhead dropped because the process was self-sustaining once the training framework was in place.

Scope of Coverage

Bi-weekly regression runs covered the full Vivint app and connected hardware — 2M+ active users meant bugs in production had real, visible consequences. The cycles had to be thorough and on schedule.

When Apple or Google shipped a new OS version, I headed compatibility testing across all product lines before it hit the public. Catching regressions at that stage — before users upgraded — was one of the highest-leverage QA activities in the team’s calendar.

New Product Launches

Taking ownership of QA for smart door locks and Philips Hue integrations meant working closely with product managers to align test coverage with release timelines, and authoring new test cases from scratch for hardware-software interactions that hadn’t been tested before.

I also maintained the existing test suite across the whole product line — keeping it current as features shipped and ensuring inherited cases reflected the product as it actually was, not how it was six months ago.

The Role

Vivint was my entry point into the software industry. The team owned quality across a wide product surface — smart locks, cameras, thermostats, Philips Hue integrations, and the main app serving millions of users.

From day one the role demanded structured thinking: writing test cases, tracking regressions, filing clear bug reports, and coordinating with developers on build testing. It was a strong foundation for understanding how software ships at scale.