Kitco — Frontend Engineering, Financial Data UX & Cloud Reliability

Overview

Kitco is a high-traffic financial and precious metals platform combining real-time pricing, market charts, editorial content, media, and community features.

I worked across multiple services with a focus on frontend delivery, financial data experiences, performance, production reliability, and cloud scalability.

Scope of engagement

Frontend engineering, financial data UX, API performance, cache strategy, and platform reliability across core web properties and supporting services.

What I owned

• Delivered frontend work on a large Next.js application serving investor-facing pricing and market-data experiences.
• Worked on chart-driven financial interfaces designed to surface live market information clearly and reliably.
• Improved caching and request-handling strategy to reduce backend pressure on high-traffic market-data paths.
• Contributed to symbol import, indexing, and synchronization workflows supporting charting and market discovery experiences.
• Diagnosed and resolved production issues affecting availability, performance, and runtime reliability under live traffic.
• Improved Kubernetes-based service reliability through readiness, scaling, rollout-safety, and traffic-handling enhancements.
• Helped onboard engineers into the frontend architecture, deployment workflow, and production model.

Financial data and charting

A meaningful part of the work involved building and supporting investor-facing market-data experiences: pricing pages, chart integrations, symbol-driven workflows, and supporting services responsible for keeping financial data reliable and usable in production.

This included chart-heavy interfaces, frequently updated pricing experiences, symbol ingestion and synchronization, and reliability improvements in the systems behind searchable market data and chart integrations.

Performance and reliability

Implemented cache and traffic-management improvements that reduced origin load, improved resilience during traffic spikes, and made critical market-data flows more reliable in production.

Worked directly on production issues across the frontend/platform boundary, improving cache behavior, service readiness, rollout safety, and runtime stability.

Selected engineering wins

• Upgraded the search platform from end-of-life Solr 8 to Solr 9 with zero downtime: search configuration baked into a reproducible container image, the full cutover rehearsed in dev first, and production monitored live for hours before handoff.
• Root-caused a production outage where the cache layer served stale HTML error pages for JavaScript assets after deploys — posted the structured postmortem within minutes of the incident ticket, then fixed the cache policy and deploy-time purge so the failure class could not recur.
• Diagnosed why saturated pods accumulated instead of recovering: the deployment had no readiness probe. Added one and measured the difference — healthy pods answer in 2-6 ms, stuck pods time out — so the platform now heals itself.
• Normalized caching behavior across layers with stale-while-revalidate grace, so APIs degrade gracefully under backend pressure instead of failing hard.
• Authored and executed an 11-task cloud cost-reduction program: eliminated 30-second polling of once-daily data, right-sized a 17-node cluster, and tuned autoscaling and resource limits off the throttling edge.
• Operate the workloads on a regional three-zone GKE cluster with per-workload node pools autoscaling from 5 to 60 nodes under traffic spikes.

Platform scale

High-traffic investor-facing platform supporting a globally distributed audience with real-time market data, chart experiences, and a strong organic traffic footprint.

Outcome

This work improved the resilience of investor-facing financial data experiences, reduced recurring production risk on high-traffic paths, and strengthened the operational reliability of the platform behind the user-facing product.