Expertise · No. I
I've owned ByteNite's product end-to-end: from the first customer interview to production, through a full market pivot, and across a cross-functional team. My product practice is grounded in direct user research, rigorous backlog prioritization, and a bias toward shipping and measuring over speculating.
I owned every phase of ByteNite's product from day one. Discovery started with structured interviews with AI developers and infrastructure engineers to identify where existing tools fell short. I translated those insights into technical specifications, scoped them into sprint-ready tickets, shipped iteratively, and tracked outcomes against real signals from users.
ByteNite docsRedesigned the developer onboarding path by translating complex container orchestration into JSON-based templates, intuitive step-by-step guides, and clonable repositories. The result: a 60% reduction in onboarding time as measured by time-to-first-successful-deploy with our users. The insight behind it: when competition is harsh, get a user to working prototype as fast as possible to lock in their initial awe and turn it into commitment.
View onboarding guidesBuilt and maintained Metabase dashboards over production data to surface hidden system inefficiencies and real usage patterns. Wrote SQL-based analyses that revealed bottlenecks invisible to the engineering team's intuition, then drove the prioritization decisions that led to a 50% increase in computing throughput. Good product analytics is not a reporting function: it's how you find the decisions worth making.
View QA discussionBuilt comprehensive cloud cost-forecast models by synthesizing multi-vendor pricing data across AWS and GCP. Implemented standardized provisioning rules and spending guardrails that reduced compute expenses by 30% without degrading performance SLAs. Treating infrastructure cost as a product constraint rather than a pure engineering concern enabled sustainable unit economics that could be extended to any scale.
In 2023, customer feedback and market signals made it clear our video encoding SaaS was hitting a ceiling: a narrow segment, high competition, and limited willingness to pay. Rather than optimizing a product that wasn't working, I ran a structured discovery sprint and identified a broader, underserved need: general-purpose serverless compute for AI and data workloads. I built the case for the pivot off of market standards and engineering insight, restructured the roadmap, and led the transition from a vertical SaaS to a horizontal Platform as a Service. The pivot expanded our addressable market and directly shaped our successful Techstars application.
As product owner in a small, high-velocity team, I coordinated engineering, go-to-market, and QA from a single source of truth: the product spec. I wrote requirements that engineers could build, defined quality criteria that QA could test, and shaped the value narrative the GTM team could sell. I ran weekly reviews and facilitated retrospectives, maintaining shared context across a distributed team. When technical trade-offs created friction, I anchored resolution to product goals rather than individual preferences.
Maintained a living product roadmap fed by three inputs: direct customer feedback, competitive market analysis, and infrastructure capacity constraints. Ran structured 6-week planning cycles using effort/impact scoring to surface the highest-leverage tickets. Tracked the developer platform landscape actively, including positioning against emerging AI compute providers, to keep our differentiation sharp. Every major roadmap decision was backed by a written rationale tied to specific user problems and business outcomes.