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Juan Linares

@juanlinares

Software Engineer with 4+ years building high-throughput C#/.NET microservices and Kafka pipelines for real-time fintech reliability.

United States

Message

What I'm looking for

I’m looking to deepen my expertise in Azure/GCP-based fintech, event-sourcing, and Kafka-driven workflows—especially where fraud/risk systems matter—while building reliable, observable microservices.

I’m a Software Engineer with 4+ years of experience delivering high-throughput C#/.NET Core microservices and Kafka event pipelines in high-scale, transactional environments. I’ve managed 10M+ monthly transactions across distributed domains including Tendering, Visibility, and Financial Settlement, and I stay focused on making systems dependable under real production load.

I modernize legacy monoliths into vertical slice microservices and optimize data flow with GraphQL/Apollo. In my recent work, I improved API response times by 40% using Redis caching, query optimization, and New Relic-based monitoring to enable reliable, real-time transaction visibility.

I build with system-first design reliability—using SonarQube 80% coverage gates, Argo CD-driven CI/CD automation, and observability practices to reduce rollout risk. I also partner across engineering, QA, and product, leveraging automated testing (xUnit/NUnit, Moq, Cypress) and feature flags (LaunchDarkly) to ship confidently and iterate quickly.

Experience

Work history, roles, and key accomplishments

Software Engineer

Mastery Logistics Systems

Aug 2022 - Sep 2025 (3 years 1 month)

Contributed to C#/.NET microservices processing 10M+ transactions per month via Kafka event pipelines on Azure. Improved API response times by 40% using Redis caching and New Relic monitoring, and automated Docker/Kubernetes deployments with Argo CD to cut deployment time by 50%.

Software Engineer

Ahern Family of Companies

Jan 2021 - Aug 2022 (1 year 7 months)

Built and maintained C#/.NET REST APIs for payment- and workflow-driven services on SQL Server, supporting 1,800+ internal users. Improved database performance by 30%, maintained 80% unit test coverage, and refactored legacy code into vertical slices using Kafka.