WHO YOU’LL WORK WITH

You will be part of Nike’s Supply Chain and Planning Technology (SCPT) organization supporting Demand Planning and Pricing (DPP) platforms. As a Lead Software Engineer, you will report to an Sr. Engineering Manager and collaborate closely with Demand and Assortment Planners, 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 DPP.

This role is critical to building and scaling Nike’s Planning platform - enabling reliable, scalable, and extensible systems that support enterprise-wide capabilities across domains.

WHO WE ARE LOOKING FOR

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

• 8 - 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)

• Domain expertise in implementing solutions for retail apparel industry specific demand planning, forecasting, and pricing capabilities

• Strong expertise in Full-stack software development and systems architecture

• Proven experience designing and operating large-scale distributed systems including API Design, Integration and Microservices, Database systems and Data Modeling, Security, Compliance and data privacy

• Hands-on experience with event-driven architecture and messaging systems (Kafka, RabbitMQ, or similar)

• 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

• Solid experience with Cloud infrastructure and deployment patterns (AWS preferred) and cloud-native design principles

• Strong understanding of CI/CD, containerization (Docker/Kubernetes), and DevOps practices

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 directly work with the business users.

• Ability to adapt and operate effectively in a continuously changing, fast-paced, ambiguous environment

• Bring a strong mindset of Forward Deployment Engineer to implement &  deliver at speed and scale

WHAT YOU’LL WORK ON

As a Lead Software Engineer, you will drive strategy, architecture, and execution for Demand planning and pricing 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 Data Driven, 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

• Strategize and design Ontology based explainable systems that can drive decisions and insights at high accuracy

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 Business, 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