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Backend Engineering Team Lead
Not specifiedNot specifiedBackend Development
Lead and contribute to backend engineering in a product-focused research and development organization. The role combines approximately equal hands-on development and team leadership, with broad influence across engineering practices and architecture.
Responsibilities
- Design, build, and operate production-grade backend systems
- Lead architecture for cloud-native, distributed, and data-intensive services
- Contribute hands-on to backend development while managing and mentoring engineers
- Define and improve service architecture, data flows, scalability, reliability, and performance
- Design and operate microservices infrastructure using Kubernetes and Docker
- Build and maintain CI/CD pipelines in microservice and monorepo environments
- Work with cloud services including Cloud Run, BigQuery, GCS, and IAM
- Use orchestration tools such as Airflow where required
- Improve engineering processes and accelerate technical delivery across the development organization
- Collaborate closely with executive technical leadership on architecture and infrastructure security
Requirements
- At least 5 years of backend engineering experience building and operating production systems
- At least 2 years of engineering leadership experience mentoring and managing developers
- Strong experience designing scalable distributed systems, including microservices and event-driven architectures
- Experience with high-scale, data-intensive systems, with emphasis on performance and reliability
- Hands-on experience with cloud environments such as GCP, AWS, or Azure
- Strong understanding of system design, data flows, and service architecture
- Experience with Kubernetes, Docker, Linux, and production microservices
- For the security-focused scope, prior cybersecurity experience is required
- For the platform-focused scope, at least 6 years of hands-on Python development and 3 years of team leadership experience
Nice to have
- Experience with complex domains and messy real-world data
- Familiarity with AI/ML-driven systems or enabling infrastructure
- Experience with cloud cost optimization or resource management