Backend Tech Lead

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 7+ years of professional software engineering experience, with a strong track record of building and operating production backend systems at scale.
• Demonstrated experience leading both projects and people — owning end-to-end delivery for a team, managing engineers directly, and mentoring juniors. You must be able to point to specific teams you’ve grown and projects you’ve driven to delivery.
• Languages & Frameworks: Deep expertise in at least one major backend stack (e.g., Java/Spring Boot, Go, Python, Node.js, or C#/.NET) and the ability to work fluently across more than one.
• Distributed Systems & Microservices: Strong, demonstrable experience designing and operating microservices and distributed systems in production.
• APIs: Hands-on expertise designing and evolving APIs (REST, gRPC, or GraphQL), including contracts, versioning, and documentation.
• Messaging & Async: Practical experience with message queues / event streaming (Kafka, RabbitMQ, SQS, etc.).
• Data: Strong SQL skills and solid experience with relational databases, plus working knowledge of NoSQL stores and data modeling for scale.
• Caching: Hands-on experience with caching strategies and tools (Redis, Memcached).
• Containers & Orchestration: Hands-on experience with Docker and Kubernetes.
• Networking: Solid understanding of networking fundamentals — HTTP/HTTPS, TLS, DNS, service discovery, and load balancing.
• Version Control & Practices: Strong Git/GitOps proficiency and a deep commitment to code review, testing, and CI/CD.
• Cloud: Demonstrable experience building backend systems on at least one major cloud platform (AWS, Azure, or GCP).
• Preferred
• Experience with service mesh, API gateways, or platform/internal-developer-platform tooling.
• Experience with performance profiling, load testing, and capacity planning.
• Familiarity with infrastructure-as-code and partnering closely with DevOps/SRE.
• Exposure to AI-driven or data-intensive backend workloads, as Accellor increasingly integrates backend engineering with AI-powered platform delivery.