Summary
✨ AI‑Generated
An experienced Java backend engineer is sought to build and operate high-performance data services. You will develop production systems using Spring-based technologies, streaming and distributed data tools, relational and NoSQL databases, and time-series storage while focusing on concurrency, performance, and reliable delivery.
Highlights
Advanced backend engineering role involving high-frequency data processing, distributed data systems, performance optimization, and modern AI-assisted development practices.
Description
創立於1993年美國矽谷，專注於寬頻接取與家庭網路解決方案，主要服務北美電信業者，累計出貨超過6,800萬台聯網設備，並獲得220多項產業獎項。
2024年12月，正式成立台灣子公司，聚焦於新一代網路設備的韌體開發與測試，持續拓展全球研發與工程能力。為 Cambridge Industries USA Inc.
全資子公司，隸屬於 CIG（Cambridge Industries Group），結合全球資源與技術實力，提供高效能、易於部署的網路產品與解決方案
5–8+ years of professional Java backend development with strong proficiency in Spring Boot, JPA, and MyBatis/Hibernate Solid hands-on experience building or operating Spark-based data processing workloads in production Practical experience with time-series databases (TDengine, InfluxDB, TimescaleDB, or equivalent) Proven experience integrating Kafka, MySQL, and MongoDB in production environments Strong understanding of high-frequency data processing, performance tuning, and concurrency control Proficiency with Git, Maven/Gradle, and CI/CD pipelines in team-based development Demonstrated ability to integrate AI tools into daily engineering workflows — including spec-driven practices such as defining architecture, constraints, and acceptance criteria before writing code — as consistent practice, not occasional experimentation Design, build, and optimize backend services using Java (Spring Boot) for high-throughput, data-intensive platforms Develop and operate batch and real-time data pipelines using Apache Spark Architect and manage time-series data solutions with TDengine or similar databases Integrate and orchestrate data flows across Kafka, MySQL, MongoDB, Hadoop, and S3-compatible storage Design high-performance, multithreaded processing models, scheduling mechanisms, and monitoring strategies Apply AI tools throughout design, code generation, refactoring, performance analysis, and incident investigation Practice Spec-Driven Development — define intent, architectural constraints, and acceptance criteria before implementation, using tools like GitHub Spec Kit to make specifications the single source of truth for AI agent code generation and validation Produce clear technical documentation including system diagrams, data flow designs, and architectural decision records Contribute to cross-language modules in Golang when applicable Collaborate with senior engineers to co-evolve platform architecture and data strategy
