Mid-level Backend Engineer specializing in microservices and event-driven systems
Mclean, VABackend Engineer4 years experienceMid-LevelRetailFood & BeverageRestaurants
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About
Backend-leaning full-stack engineer who has built and operated event-driven microservices platforms (FastAPI/React/TypeScript, Kafka, Kubernetes) and internal DevOps tooling. Delivered measurable impact through user-feedback-driven iteration (WebSockets update mechanism cutting redundant API calls ~30%) and operational improvements (deployment monitoring dashboard reducing rollback time ~40%), with strong focus on reliability, observability, and data consistency at scale.
Experience
Backend EngineerRestaurant Brands International
Software EngineerEminent Software Solutions
Software EngineerRestaurant Brands International
Education
University of Maryland, Baltimore Countymaster, Computer Science (2024)
Mahatma Gandhi Institute of Technologybachelor, Electrical and Electronics Engineering (2022)
Key Strengths
End-to-end backend ownership for high-volume complaint management system with iterative MVP releases
User-feedback-driven performance improvements (implemented WebSockets; reduced redundant API calls ~30%)
Designed and delivered scalable microservices platform (Kafka, FastAPI, Kubernetes) for workflow automation
Resolved microservices data consistency issues using idempotency, event identifiers, retries with exponential backoff, and offset management
Improved observability across services with ELK centralized logging and Jaeger distributed tracing
Built and drove adoption of an internal deployment monitoring dashboard; reduced rollback time ~40%
Integrated heterogeneous DevOps data sources (Jenkins/Kubernetes/Grafana) via normalized aggregation layer and parallel async fetching
Prioritization in ambiguous, multi-stakeholder environments; reduced manual reconciliation effort ~30% via alerting/exception handling iteration
Owned end-to-end backend design and development for a real-time fitness tracker (Python/Flask + AWS)
Resolved inconsistent device data via validation/retry with SQS + Lambda
Performance optimization via caching, cutting response times by nearly half
Production Kubernetes microservices deployment on AWS EKS using Helm (autoscaling, probes, resource limits)
Built CI/CD pipelines (GitHub Actions/Jenkins) to ECR and automated EKS deployments; implemented GitOps with Argo CD and rollback
Solved failed rollouts by improving readiness/health probes and enabling Argo CD auto-rollbacks
Implemented cross-environment secrets management using AWS Secrets Manager integrated with Kubernetes Secrets