Senior Backend JavaScript Developer
Bright Data Beijing, Beijing, China
Software Development · 201-500 employees
About the role
Design, build, and operate distributed ETL and data processing pipelines using Node.js and AWS. Own technical solutions end-to-end while ensuring system performance, scalability, and production-readiness.
What they look for
Requirements
Requires 5+ years of software engineering experience with at least 3 years focused on backend JavaScript and Node.js. Candidates must have strong system design skills and experience working with distributed, data-intensive cloud systems.
Full description
We’re looking for a Senior Backend JavaScript Developer to join our Web_archive team and help build and evolve a high-throughput data processing platform.
You will own distributed data pipelines and backend services end-to-end—from technical design and implementation to production operation and continuous improvement. This role requires strong Node.js expertise, solid system-design skills, and the ability to quickly adapt to new technologies and changing requirements.
Our primary backend technology is Node.js, and our infrastructure runs mainly on AWS.
The current environment includes:
- AWS S3 for large-scale object storage
- RabbitMQ and AWS SQS for messaging and asynchronous processing
- Kubernetes for container orchestration
- Snowflake for analytical workloads and large SQL queries
- MongoDB and other NoSQL or relational databases
- Redis for high-load caching and key-value storage
- Rust for selected performance-critical components
Our stack continues to evolve as we adopt AWS-native services and adjust our architecture to changing scale and reliability requirements. Exact experience with every technology is not required- we value strong engineering fundamentals and the ability to learn and apply new tools quickly.
Responsibilities
- Design, build, and operate distributed ETL and data processing pipelines.
- Develop production-grade backend services using Node.js and AWS.
- Own technical solutions from design through deployment, monitoring, and ongoing improvement.
- Diagnose and optimize performance, including event-loop behavior, streams, backpressure, memory usage, and resource utilization.
- Build resilient asynchronous workflows with appropriate retry, failure-handling, and observability patterns.
- Work with large datasets across object storage, analytical platforms, caches, and operational databases.
- Contribute to architecture, code reviews, testing practices, and production-readiness standards.
- Use AI-assisted development tools responsibly, applying appropriate guardrails such as scoped permissions, controlled access to code and data, review gates, and validation before production deployment.
- Participate in the team’s on-call rotation and help investigate and prevent production incidents.
Requirements
- 5+ years of software engineering experience, including 3+ years focused on backend JavaScript and Node.js.
- Experience building and operating production backend services or SaaS applications.
- Strong understanding of Node.js under load, including the event loop, streams, backpressure, and memory management.
- Experience designing distributed, high-throughput, or data-intensive systems.
- Practical understanding of asynchronous processing, message queues, delivery guarantees, retries, and failure handling.
- Experience working with cloud infrastructure, containerized deployments, and production observability.
- Ability to work with different data systems, including analytical SQL platforms, NoSQL databases, object storage, and high-load caches.
- Good understanding of system design, scalability, reliability, data modeling, and performance optimization.
- Strong ownership and the ability to independently drive technical work from design to production.
- Ability and willingness to learn new technologies based on engineering and product needs.
- Strong written and verbal communication skills in English.
- Practical experience with AI-assisted development workflows and a strong understanding of their risks and limitations. You are expected to retain ownership of the resulting code, treat AI-generated output as untrusted, verify it through code review and automated testing, and apply appropriate security guardrails around access to source code, data, credentials, tools, and production systems.
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