You will be part of Nike’s Supply Chain and Planning Technology (SCPT) organisation supporting Order Flow Management (OFM) platforms. As a Lead Software Engineer, you will report to an Sr. Engineering Manager/Director and collaborate closely with Principal Engineers, Product Managers, and cross-functional partners.
You will act as the technical leader for your squad, guiding and mentoring engineers while working across teams to deliver scalable platform capabilities across OFM. This role is critical to building and scaling Nike’s OFM platform - enabling reliable, scalable, and extensible systems that support enterprise-wide capabilities across domains.
Education & Experience
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
7 - 12 years of experience building scalable backend systems
2+ years in a technical leadership role, guiding teams and driving architecture decisions
Technical Expertise (Core - Required)
Strong backend expertise in Java, Spring Boot, and microservices architecture
Proven experience designing and operating large-scale distributed systems and REST APIs
Hands-on experience with event-driven architecture and messaging systems (Kafka, RabbitMQ, or similar)
Solid experience with cloud platforms (AWS preferred) and cloud-native design principles
Strong understanding of CI/CD, containerization (Docker/Kubernetes), and DevOps practices
Additional Capabilities (Preferred - Not Mandatory)
Exposure to data engineering ecosystems (Spark, Databricks, or similar)
Strong fundamentals in SQL and data modelling
Understanding of batch and real-time data processing patterns
Exposure to frontend technologies such as React or Angular
Experience working on enterprise-scale platforms and integrations
Familiarity with distributed systems and external service integrations
Leadership & Behavioral Skills
Strong ability to mentor and influence engineering teams
Proven experience driving technical roadmaps and cross-team alignment
Excellent communication and stakeholder management skills
Ability to operate effectively in a fast-paced, ambiguous environment
As a Lead Software Engineer, you will drive strategy, architecture, and execution for backend systems that power core OFM platform capabilities.
Technical Leadership & Strategy
Own and drive the technical roadmap for your domain in partnership with Product and Architecture
Translate complex business requirements into scalable technical solutions
Lead architectural design decisions and technical reviews across systems
Backend System Design & Development
Design and build highly scalable backend systems and microservices using Java, Spring Boot, and cloud-native patterns
Develop robust RESTful APIs and event-driven services for enterprise-scale platforms
Build resilient distributed systems leveraging messaging platforms like Kafka or similar
Data Engineering & Integration (Preferred Strength Area)
Design and implement data pipelines and integrations across systems and platforms
Work with modern data platforms (e.g., Spark, Databricks, Delta Lake or equivalent) where required
Contribute to data quality, consistency, and availability across services
Cloud & Platform Engineering
Lead development and deployment of services on cloud platforms (AWS preferred), ensuring scalability and efficiency
Drive adoption of CI/CD pipelines, observability, and DevOps best practices
Engineering Excellence
Establish and enforce high standards in coding, testing, and system reliability
Continuously improve system performance and scalability
Promote modern engineering practices and platform-first thinking
Collaboration & Mentorship
Mentor engineers across levels and foster a strong engineering culture
Partner with Product and Engineering leaders to align priorities and execution
AI & Engineering Productivity
Leverage AI-assisted development tools (e.g., Copilot, code assistants) to improve coding efficiency, testing, and documentation
Identify opportunities to apply AI/GenAI capabilities to enhance system workflows, automation, and user experiences
Contribute to establishing best practices for responsible AI usage, ensuring security, data privacy, and reliability
Balance AI usage with strong engineering fundamentals, system design, and code quality standards