Software Engineer III at Mindbody (2023-09 – 2026-03)
Enterprise-scale distributed SaaS platform supporting scheduling, payments, customer engagement, and operational workflows across millions of users and thousands of businesses daily. Led modernization of backend infrastructure and cloud-native platform architecture to improve scalability, resiliency, deployment reliability, observability, and operational efficiency across high-volume production systems.
- Architected and scaled distributed backend platform services supporting high-volume scheduling, payment processing, and customer engagement workflows across production infrastructure serving millions of users daily under sustained peak traffic workloads.
- Led large-scale migration of legacy monolithic applications into Kubernetes-based microservice architecture using Python, Go, Docker, and AWS, improving deployment reliability, service isolation, operational resiliency, and engineering release velocity across critical production services.
- Designed distributed asynchronous processing pipelines for transactional and booking workflows using event-driven architecture patterns, improving resiliency during peak traffic conditions while reducing downstream service contention and operational bottlenecks.
- Reduced API p95 latency by more than 30% across critical production endpoints through Redis caching strategies, PostgreSQL query optimization, asynchronous request handling, and database indexing improvements supporting high-throughput transactional workloads.
- Built reusable internal platform abstractions and backend service frameworks that standardized distributed service communication patterns, simplified onboarding, and improved engineering consistency across backend teams.
- Improved production reliability by implementing observability standards using New Relic, CloudWatch, distributed tracing, centralized logging, and proactive alerting workflows, significantly reducing recurring production incidents and improving operational visibility across distributed systems.
- Managed AWS-based production infrastructure across EC2, RDS, S3, IAM, Kubernetes, and CI/CD deployment pipelines supporting highly available cloud-native environments under demanding production workloads with strong operational reliability standards.
- Partnered with infrastructure and engineering leadership teams to define scalability roadmaps, modernization initiatives, deployment standards, and backend architecture strategies across core platform systems.
- Mentored junior engineers through architecture reviews, debugging sessions, operational incident analysis, and engineering best practices, improving delivery consistency and code quality across projects.
Software Engineer III at ZipRecruiter (2021-06 – 2023-06)
Large-scale distributed backend platform powering onboarding, recommendation, and job discovery workflows across a hiring ecosystem serving millions of active users and high-volume search traffic under demanding production workloads.
- Engineered high-concurrency backend services in Go powering onboarding, recommendation, and search workflows across distributed production systems serving millions of monthly active users and high-volume search infrastructure.
- Developed distributed APIs and backend processing services handling thousands of requests per second with strong focus on scalability, resiliency, fault tolerance, low-latency response times, and operational stability under heavy production traffic conditions.
- Drove migration initiatives from legacy monolithic systems to Kubernetes-based microservice architecture, improving deployment consistency, infrastructure scalability, and long-term maintainability.
- Improved onboarding completion and platform responsiveness by optimizing backend orchestration workflows and frontend rendering performance across React and TypeScript applications.
- Designed and maintained REST APIs enabling reliable communication between frontend clients, recommendation services, internal platform tooling, and analytics infrastructure.
- Built containerized deployment workflows using Docker, Kubernetes, GitHub Actions, and CI/CD tooling, reducing manual operational overhead and improving deployment reliability.
- Enhanced observability across production services through centralized logging, telemetry instrumentation, distributed monitoring, and incident-debugging improvements using modern observability tooling, improving operational response efficiency during high-severity production incidents.
- Collaborated with cross-functional product, infrastructure, and platform teams to deliver scalable customer-facing systems in fast-paced production environments.
- Contributed to backend reliability initiatives reducing deployment rollback frequency and improving production stability across critical distributed services.
Software Engineer at Arista Networks (2018-12 – 2021-06)
Enterprise-scale telemetry and observability platform processing millions of real-time infrastructure and networking events daily across distributed cloud environments and large-scale enterprise production systems.
- Engineered backend services in Go and Python for distributed ingestion, aggregation, processing, and management of high-volume streaming telemetry data across cloud-scale infrastructure and observability systems.
- Designed scalable APIs and distributed workflows supporting low-latency telemetry retrieval, operational visibility, and infrastructure observability across enterprise-scale networking environments.
- Developed React-based operational dashboards visualizing real-time infrastructure metrics, telemetry insights, and network health indicators for enterprise customers.
- Built and deployed containerized cloud-native services using Docker and Kubernetes to support horizontally scalable infrastructure systems with improved operational resilience and deployment consistency.
- Improved operational troubleshooting workflows by enhancing logging pipelines, monitoring coverage, telemetry visibility, and incident investigation tooling across distributed infrastructure services.
- Participated in architecture and scalability initiatives focused on distributed systems reliability, telemetry throughput optimization, fault tolerance, production performance engineering, and operational resiliency across large-scale infrastructure environments.
- Supported optimization initiatives improving telemetry processing efficiency and reducing bottlenecks across high-volume data ingestion pipelines operating under sustained production traffic.
Backend Engineer at Driver (2018-07 – 2018-10)
- Developed Python backend services supporting healthcare workflow management, patient operations, and treatment-processing systems.
- Built data ingestion and normalization pipelines processing structured medical records and operational healthcare datasets.
- Designed relational data models representing patient, treatment, and clinical workflow relationships across backend systems.
- Implemented REST APIs powering internal operational tools used by healthcare, research, and clinical teams.
- Collaborated with domain stakeholders to translate healthcare workflow requirements into scalable backend platform solutions.
- Supported production debugging, backend maintenance, and reliability improvements across internal healthcare services.
