Vinícius Stocco
Software Engineer @Smartsheet
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WORK HISTORY
Software Engineer @Smartsheet
London, GB
Scaled CDC pipeline with 70+ Kinesis shards across 5 regions, processing high-volume event streams with sub-3s average database lag.• Designed and implemented event-driven backend services using Go/Java on AWS, integrating various AWS services for efficient data propagation. • Deployed and scaled services on Kubernetes (EKS) with Helm, FluxCD, and Kustomize, implementing GitOps practices and infrastructure as code with Terraform/Terragrunt.• Defined and created SLOs, SLI and SLA with leadership, establishing metrics, DataDog dashboards, and runbooks to track error budgets and ensure operational excellence.• Mentored engineers through pair programming and comprehensive documentation (design reviews, operational readiness docs, test strategies), accelerating onboarding and reducing pre-production defects.• Automated the shard onboarding process with Python and GitLab API, reducing deployment time significantly and minimizing configuration errors.
EDUCATION
USP - Universidade de São Paulo
Bachelor’s Degree, Computer Science
University of Leeds
Master's degree, Artificial Intelligence
ABOUT VINÍCIUS STOCCO
Currently based in England with settled status, I am a computer scientist who graduated from the University of São Paulo and holds an MSc in Artificial Intelligence from the University of Leeds.I specialize in distributed systems and event-driven architecture, with commercial experience building scalable backend platforms using Apache Flink, Kafka, Kinesis, and Kubernetes. My expertise spans real-time data processing, CDC pipelines, microservices architecture, and cloud infrastructure on AWS. I work primarily with Go, Java, and Python, and have hands-on experience with Terraform, GitOps, REST APIs, and both SQL and NoSQL databases. I have scaled infrastructure using Kubernetes and FluxCD across multiple environments and regions.During my MSc at the University of Leeds, I specialized in deep learning and neural networks, implementing transformer architectures, CNNs, and graph neural networks using PyTorch. I worked with reinforcement learning algorithms including Q-learning and PPO, built deep Q-networks for autonomous agent control, and explored probabilistic models, Bayesian methods, and generative models.
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