软件工程师方向
实习项目
Led the migration of a log aggregation system from Kafka Stream to Flink, reducing log processing latency from under 15 seconds to 1 second and increasing throughput from 1 million to over 5 million messages per second, enabling faster analysis and reporting for critical applications.
Refactored a framework-agnostic Dead Letter Queue (DLQ) library using Kafka and Prometheus, standardizing log failure formats and achieving a centralized log collection system with a failure detection rate exceeding 90%.
Designed and implemented RESTful APIs powered by Elasticsearch to query and filter logs, reducing troubleshooting time by 40% per ticket for support engineers by streamlining the search for blocking events and DLQ topics.
Implemented a real-time rule injection server with gRPC APIs, enabling dynamic injection of log filtering and processing rules into the log pipeline, facilitating on-the-fly log transformation without impacting system availability.
Built a monitoring service using Kotlin, Spring Boot, and PostgreSQL to track pipeline errors and log failures, providing real-time notifications to support engineers, thereby reducing the average issue resolution time by over 30%.
Created and validated 23 PromQL queries for a Grafana dashboard, integrating customer-facing APIs to enhance monitoring capabilities across over 100 system metrics, improving visibility into the system's health and performance.
Partnered with cross-functional teams, including infrastructure and DevOps, to deploy the system using Terraform and ensure scalable and fault-tolerant operation, supporting rapid growth in log data volume.
Organized knowledge-sharing sessions with engineering teams to onboard new services onto the log aggregation system and optimize troubleshooting workflows, fostering organization-wide adoption.
Designed and implemented an end-to-end feature flag management system using Spring Boot to manage feature rollouts for over 30 microservices and 800+ pods, leveraging GCP Pub/Sub to enable real-time feature toggles within 10 seconds, ensuring seamless updates without requiring service restarts.
Established a scalable Firestore NoSQL database for storing feature flag states, reducing manual intervention by 80% and minimizing errors caused by misconfigured flags, resulting in more reliable feature rollouts.
Developed a React-based frontend dashboard to manage feature flags in a user-friendly interface, enabling real-time visibility into active flags and allowing non-technical stakeholders to monitor and control feature rollouts.
Collaborated with the infrastructure team to deploy the feature flag management system in a decoupled environment using Terraform, managing secrets, environment variables, and CI/CD pipelines through Vault and Buildkite, ensuring smooth integration and scalable deployments.
Partnered with cross-functional teams to define flagging strategies, enabling controlled rollouts (e.g., percentage-based or role-based toggles) and ensuring alignment between engineering, product, and QA teams.
Conducted training sessions and live demonstrations to help teams integrate feature flagging into their workflows, showcasing strategies for using the feature flag system to enable blue-green deployments, A/B testing, and canary releases.
Implemented role-based access control (RBAC) for secure management of feature flags, ensuring only authorized users can modify or deploy sensitive feature rollouts.
Engineered a high-performance graphics engine using C++, integrating a SIMD math library, and achieving a 50% acceleration in runtime speed for matrix transformations and vector calculations through parallel computations.
Optimized the rendering pipeline, executing matrix transformations for dynamic graphics rendering, and developed custom shaders using HLSL, implementing advanced lighting models such as physically based rendering (PBR) and shadow mapping.
Designed and implemented a vertex management system from the ground up, enhancing the graphical rendering pipeline with efficient low-level buffer upload mechanisms and optimized data formats, reducing rendering overhead.
Improved rendering performance by 20.3% in scenarios with high character or object counts using multithreaded programming techniques, balancing workload distribution across CPU cores.
Enhanced system scalability and performance through advanced resource management techniques, including batch rendering and geometry instancing, which reduced draw calls by 35%, improving rendering throughput.
Collaborated with a team of designers and developers to align rendering engine optimizations with artistic requirements, ensuring seamless integration of advanced visual effects without compromising performance.
Worked closely with cross-functional teams to debug and profile the engine using NVIDIA Nsight and RenderDoc, identifying performance bottlenecks and implementing improvements that increased frame rates across various test scenarios.
Designed a custom memory allocator to improve memory access patterns, reducing memory fragmentation by 25% and improving overall system efficiency in real-time graphics applications.
Developed and managed the backend for a Real-Time Collaboration Platform to support document sharing, task management, and team messaging. Crafted 20+ RESTful APIs for features such as user management, workspace creation, and real-time task updates using Spring Boot.
Deployed the application on Amazon ECS with auto-scaling capabilities to efficiently handle varying user loads, maintaining high availability during peak usage.
Integrated PostgreSQL for relational data storage, Amazon S3 for secure document storage, and Amazon ElastiCache (Redis) to reduce data retrieval latency by 40.2% and enhance platform responsiveness.
Improved service observability and security by implementing aspect-oriented programming (AOP) for real-time metrics emission to Amazon CloudWatch, along with unified authentication and role-based access control to safeguard sensitive data.
Designed a serverless architecture on Amazon Lambda for real-time task notifications, leveraging Amazon API Gateway WebSocket API, SNS, SQS, and DynamoDB for low-latency message delivery and fault-tolerant workflows. Achieved 35% faster notification delivery compared to traditional polling mechanisms.
Optimized backend performance with advanced techniques, including connection pooling, query optimization, and data caching, reducing API response times by 32.7% and supporting 3.5x higher concurrent user capacity.
Ensured high code quality by implementing unit and integration tests with JUnit, Mockito, and Spring MockMVC, achieving 85%+ test coverage, which significantly reduced production bugs by 40%.
Automated continuous integration and deployment pipelines using GitHub Actions, Amazon CodeDeploy, and Amazon CodePipeline, reducing deployment times by 42.5% and ensuring minimal downtime during releases.
Actively engaged in project prototyping, provided technical guidance, and conducted peer code reviews, fostering a collaborative development environment and improving team productivity by 20%.
Designed and developed a full-stack Real-Time Inventory Management System to track product stock levels, monitor warehouse operations, and provide real-time inventory insights. Utilized Microservices architecture, including inventory data receiver and update handler using NodeJS, JavaScript, TypeScript, and NextJS.
Integrated PostgreSQL for relational database management and improved query performance by 27.4% using indexing and query optimization techniques. Incorporated Amazon S3 for backup storage and reduced file retrieval latency by 38.6% using pre-signed URLs.
Leveraged Twitter API for automated alert notifications on stock depletion and integrated Google Maps API, reducing warehouse location rendering times by 18.7%, improving logistics planning efficiency.
Enabled real-time synchronization of inventory data across users with WebSockets, reducing data latency for updates by 52.3% compared to traditional polling methods.
Developed a robust backend server using NodeJS, implementing optimized CRUD RESTful APIs, reducing API response times by 34.8% through improved query handling and connection pooling.
Built an interactive client-side dashboard for tracking inventory, managing user roles, and authorizations. Utilized React, Redux, and Material-UI to deliver a responsive and intuitive user interface. Applied code splitting and lazy loading, reducing initial page load times by 24.5% and enhancing user experience during high traffic periods.
Hosted and scaled the application on AWS infrastructure, utilizing Elastic Container Service (ECS) with Docker containers, API Gateway, and Elastic Load Balancer (ELB) to ensure 99.92% availability and support for concurrent user scaling.
Implemented AWS VPC with optimized Subnets and NAT Gateway configurations. Enhanced security with fine-grained IAM policies and Elastic IP configurations for reliable networking.
Established a robust CI/CD pipeline using GitHub Actions with parallelized workflows and automated testing, reducing deployment time by 41.6% and minimizing downtime during production releases.
Improved system performance with backend optimizations, including database connection pooling, query optimization, and caching strategies using Redis, reducing backend processing times by 29.3%.
汇聚1300+
在职面试官
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直通硅谷成立于2015年3月，由北大计算机系师兄联合MIT、前百度网络科技产品经理、Harvard高级学者、香港上市公司联席董事共同创立，心之所向，是壮大全球华人力量。 凭借在求职辅导中积累的丰富经验，我们不断研发顺应科技界求职趋势的学练结合课程，组建富有实战经验的国内外名企导师团队，已成功帮助超过8000+学员进入全球一线大厂。
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