Lead Backend Engineer - Golang (Product Engineering)

Oolio · India

ABOUT OOLIO:

Oolio is a leading B2B SaaS platform transforming how hospitality venues operate and grow. Trusted by more than 22,000 venues, we power mission-critical POS, payments, online ordering, kiosks, loyalty, kitchen management, and real-time insights — all within one connected ecosystem.

We are building the operating system for modern hospitality — simplifying complex operations, accelerating service, and unlocking smarter, data-driven decisions. Built by hospitality professionals with decades of industry experience, we understand the realities of every shift, every service rush, and every guest interaction. From cafés and quick-service restaurants to pubs, multi-site groups, and stadiums, Oolio enables venues to operate seamlessly at scale. With next-business-day settlements, powerful third-party integrations, and 24/7 real human support, we go beyond software — we become long-term partners in growth.

As a rapidly scaling product-led organisation, we’re shaping the future of hospitality technology.

We build the technology backbone that powers modern hospitality businesses to perform, compete, and thrive at scale.

JOB DESCRIPTION:

At Oolio, Lead Backend Engineer – Golang are hands-on technical leaders who design, build, and operate scalable, reliable, and high-performance backend systems. You will take ownership across the complete backend engineering lifecycle — from architecture and system design through development, testing, deployment, observability, and production reliability.

The role requires deep expertise in Golang, microservices, distributed systems, APIs, event-driven architectures, cloud-native technologies, and system performance. As AI continues to transform software engineering, you will also be encouraged to leverage AI-powered development tools, automation, and emerging AI capabilities to improve engineering productivity, accelerate delivery, enhance code quality, and solve real-world engineering problems.

The primary focus of this role is backend engineering and technical leadership , with AI serving as an additional capability rather than the core domain. You will work closely with Product, Design, Platform, and Engineering teams to build customer-facing products and the backend capabilities that support Oolio's growing global platform.

RESPONSIBILITIES:

• Own end-to-end development of backend services—from architecture and design to deployment, observability, and production reliability.
• Design and implement scalable, distributed microservices using Golang with strong emphasis on performance, concurrency, and maintainability.
• Architect and evolve event-driven systems using async messaging, queues, pub/sub, and streaming patterns to support high-scale, low-latency applications.
• Build and optimize RESTful and/or gRPC APIs that power web, mobile, and third-party integrations.
• Contribute across the stack when required, collaborating with React and Node.js services to deliver cohesive product features.
• Develop cloud-native services deployed on AWS, leveraging Kubernetes and Docker for containerized infrastructure.
• Optimize application performance, throughput, scalability, and reliability as product usage and customer scale increase.
• Implement strong testing strategies including unit, integration, contract, and performance testing.
• Drive observability best practices including structured logging, monitoring, metrics, tracing, and alerting.
• Participate in code reviews and actively contribute to improving engineering standards and best practices.
• Troubleshoot complex production issues, perform root cause analysis, and implement long-term systemic fixes.
• Design and implement AI-powered engineering workflows that improve software delivery velocity and engineering productivity.
• Develop agentic workflows capable of interacting with code repositories, ticketing systems, deployment pipelines, and engineering platforms.
• Evaluate and integrate modern AI technologies, frameworks, and tooling into the engineering ecosystem.
• Identify bottlenecks across the SDLC and implement AI-driven solutions that improve efficiency, quality, and developer experience.
• Drive adoption of AI-native software engineering practices and continuously improve engineering effectiveness across teams.

REQUIREMENTS:

Role: Lead Backend Engineer (Product Engineering) – final role will depend on candidate experience, credentials and interview outcomes

Experience: 8 – 18 Years

Education: Preferred – B.Tech/B.E/M.Tech/M.E/MCA/M.S

Technology Stack:

(A) Core Engineering:

Golang, Microservices, Distributed Systems, Event-Driven Architecture, REST APIs, gRPC, System Design (HLD/LLD), Performance Optimization, Testing Frameworks, AI-Powered Development Tooling

(B) Good/Nice to Have:

ReactJS, NodeJS, GraphQL

OTHER REQUIREMENTS:

• Strong hands-on experience building production-grade backend systems in SaaS or product-based organisations.
• Deep expertise in Golang, including concurrency patterns, goroutines, channels, memory management, profiling, and performance optimization.
• Proven experience designing and operating scalable microservices and distributed systems.
• Strong understanding of API design, asynchronous processing, event-driven architectures, and system reliability principles.
• Experience with PostgreSQL, DynamoDB, or similar databases, including schema design, indexing, and query optimization.
• Hands-on experience with AWS, Kubernetes, Docker, and cloud-native application development.
• Strong experience implementing and improving CI/CD pipelines and modern software delivery practices.
• Strong understanding of observability, monitoring, logging, tracing, fault tolerance, and production reliability.
• Experience troubleshooting complex production issues, conducting root cause analysis, and implementing long-term improvements.
• Experience working in high-scale or high-transaction environments is highly desirable.
• Practical experience leveraging AI technologies to improve software engineering productivity and software delivery outcomes.
• Experience building or integrating AI-powered workflows across development, testing, documentation, deployment, observability, or operational processes.
• Passion for exploring emerging AI capabilities and applying them pragmatically to real-world engineering challenges.
• Ability to balance traditional software engineering principles with AI-native development practices.