Senior Backend Engineer – Platform & Data Systems

We are building the core infrastructure that processes millions of data points in real time. We are looking for engineers who treat AI as a genuine collaborator — not just a productivity tool — but as a thinking partner for system design, implementation, and quality assurance through Spec-Driven Development.

Key Responsibilities:
1. Design, build, and optimize backend services using Java (Spring Boot) for high-throughput, data-intensive platforms
2. Develop and operate batch and real-time data pipelines using Apache Spark
3. Architect and manage time-series data solutions with TDengine or similar databases
4. Integrate and orchestrate data flows across Kafka, MySQL, MongoDB, Hadoop, and S3-compatible storage
5. Design high-performance, multithreaded processing models, scheduling mechanisms, and monitoring strategies
6. Apply AI tools throughout design, code generation, refactoring, performance analysis, and incident investigation
7. 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
8. Produce clear technical documentation including system diagrams, data flow designs, and architectural decision records
9. Contribute to cross-language modules in Golang when applicable
10. Collaborate with senior engineers to co-evolve platform architecture and data strategy

【Required Qualifications】
1. 5–8+ years of professional Java backend development with strong proficiency in Spring Boot, JPA, and MyBatis/Hibernate
2. Solid hands-on experience building or operating Spark-based data processing workloads in production
3. Practical experience with time-series databases (TDengine, InfluxDB, TimescaleDB, or equivalent)
4. Proven experience integrating Kafka, MySQL, and MongoDB in production environments
5. Strong understanding of high-frequency data processing, performance tuning, and concurrency control
6. Proficiency with Git, Maven/Gradle, and CI/CD pipelines in team-based development
7. 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

【Preferred Qualifications】
1. Experience with spec-driven development tools such as GitHub Spec Kit, AWS Kiro, or equivalent workflows
2. Production development experience in Golang
3. Familiarity with containerized and cloud-native deployments (Docker, Kubernetes)
4. Experience designing distributed system architectures at scale
5. Background in monitoring, observability, or anomaly detection systems
6. Experience mentoring engineers or serving as a technical lead on complex systems

**What "AI-Assisted Engineering" Means Here**

We don't mean copy-pasting from ChatGPT. We mean engineers who:

1. Write specs before writing code — define intent, constraints, and acceptance criteria upfront so AI agents generate implementations that match what you actually meant
2. Use tools like GitHub Spec Kit to turn specifications into living blueprints that both humans and AI can interpret, refine, and execute
3. Apply code generation with review discipline — "does exactly what I asked and nothing more" is a feature, not a limitation
4. Incorporate AI-assisted refactoring and performance analysis as part of the regular development cycle
5. Share prompting strategies and spec templates that elevate the entire team's output quality