Senior Supervisor, Product & Merchandising Integration - Converse
Nike
- Location
- Shanghai, China Mainland
- Work model
- On-Site
- Level
- Senior
- H-1B history
- 111 approvals (FY2023)
- Posted
- Sep 8, 2026
About this role
WHO YOU'LL WORK WITH You will sit at the center of Converse’s Greater China Merchandising ecosystem, serving as the connective tissue between data, process, and execution, supporting the Footwear and Apparel & Accessories business units. You will partner closely with the Global Business Integration team to ensure GC nuances are represented in global tools and solutions. Locally, you will work across the Merchandising community, Business Integration GamePlan teams. You will be a cross-functional partners in Marketing, Planning, Supply Chain, and Digital. You will also collaborate with tech resources to develop local data capabilities. This role reports to the Merchandising Business Integration leadership team and requires daily engagement with stakeholders at all levels - from analysts to senior leaders .
WHAT YOU'LL WORK ON
You will own the data frameworks, quality standards, and governance processes that power GC Merchandising in Footwear and Apparel & Accessories. You'll be the central organizer who aligns teams, drives execution, and continuously improves how we work with product data. From seasonal planning cycles to strategic initiative launches, your work will directly enable faster, smarter merchandising decisions. Build the Data Foundation Classify and structure product data assets into scalable frameworks that enable consistency across all merchandising systems—from initial code setup through seasonal execution Establish mechanisms, dashboards, and tools to monitor and govern data quality, ensuring product data is accurate, timely, complete, and reliable with clear ownership and remediation paths Drive Merchandising Excellence Lead data cleansing, standardization, definition alignment, and process design. Act as the central organizer who aligns cross-functional teams (Merchandising, Planning, Supply Chain, Digital) to execute against defined processes Enable critical strategic launches through disciplined data governance from planning through execution. Identify issues, resolve them, and continuously improve by refining data structures and workflows Support regular scorecard reporting and tracking for sales performance and analysis for key merchandising events including product trend and historical sales performance Manage seasonal margin checks and provide analysis and recommendations for setting reasonable MSRPs. Develop and manage promotional guidance logic set and maintenance. Oversee sample management, including sample ordering, budget planning, coordination with logisitics teams for warehouse samples, and sample borrowing process Connect Global & Local Voice GC nuances to global Business Integration. Ensure global tools and solutions meet local business needs, and drive successful local adoption of global initiatives Close the loop by feeding local requirements and insights back to global teams so that GC needs are built in—not bolted on as an afterthought Codify, Communicate, Train Own the communication strategy for data governance outcomes. Design and deliver consistent, clear communications to increase awareness, drive adoption, and improve how teams understand and use governed product data Build and maintain the Data Governance playbook—capturing data definitions, usage guidelines, SOPs, and best practices. Deliver engaging, accessible training to upskill the broader organization from analysts to senior leaders WHO WE ARE LOOKING FOR We're looking for someone who has these experiences and has clearly demonstrated these skills: Proactive & Results-Driven: You don't wait for direction. You identify what needs to be done, mobilize resources, and drive outcomes with minimal supervision. You take ownership and deliver measurable impact with agility. Strong Logical & Strategic Thinking: You synthesize complex information, identify patterns, and draw clear conclusions. You translate business needs into structured data frameworks with a strong bias for synthesis and summarization. Strong Interpersonal &