• Drive the success and marketability of Go-To-Market (GTM) offerings • Collaborate with the Product Line Leader to identify enhancements and investments • Analyze target markets, competitors, and CMS/state mandates to inform strategy • Develop and execute client engagement plans with actionable feedback loops • Own and maintain dynamic product roadmaps aligned with client and market needs • Lead cross-functional collaboration across Delivery, Development, Operations, Marketing, and Sales • Manage initiatives with DDI teams to ensure successful GTM launches

• Définir et maintenir la stratégie produit à long terme pour les plateformes de convertisseurs d'électronique de puissance. • Piloter activement la structuration des plateformes de convertisseurs à travers les solutions HVDC et FACTS. • Développer et piloter la feuille de route produit couvrant : performance évolution, développement technologique, compétitivité des coûts, amélioration de la fiabilité, objectifs de durabilité, gestion du cycle de vie et d'obsolescence. • Assurer l'alignement de la feuille de route avec celles des solutions HVDC et FACTS. • Gérer l'intégralité du cycle de vie du produit, de la conception jusqu'au retrait. • Travailler avec les responsables du cycle de vie pour diriger les plans d'atténuation de l'obsolescence. • Suivre les développements dans les technologies émergentes et définir des stratégies d'adoption.

• Define and own the product vision, strategy, and roadmap for marketing technology capabilities • Partner with marketing leaders across multiple business areas to create alignment around a shared marketing technology strategy • Lead the transformation and expansion of Salesforce Marketing Cloud and Salesforce Data Cloud across a portfolio of brands • Translate marketing and customer needs into clear product requirements, user stories, and prioritized backlogs • Partner closely with an engineering lead and delivery teams to plan, prioritize, and execute marketing technology initiatives • Own backlog management, sprint planning, prioritization, and product decision-making within an Agile environment • Improve customer journeys across acquisition, engagement, conversion, retention, upsell, and cross sell • Guide the development of segmentation, personalization, lead management, campaign orchestration, and lifecycle marketing capabilities • Identify opportunities to use AI, Generative AI, predictive analytics, and intelligent automation to improve marketing effectiveness and internal processes • Use customer, campaign, and product data to identify problems, prioritize opportunities, and measure product outcomes • Partner with data and technology teams to support integrations between marketing platforms, CRM systems, Data Cloud, CDPs, and data warehouses • Establish KPIs and success metrics related to customer engagement, conversion, deliverability, revenue growth, and platform adoption • Support experimentation through A/B testing, multivariate testing, and continuous optimization • Promote consistent product practices, operating processes, and ways of working across marketing and technology teams • Lead and develop a small product team while remaining actively involved in strategy, requirements, and execution • Ensure marketing technology solutions support data governance, privacy, and regulatory requirements

• Own the end-to-end product vision, strategy, and roadmap for a portfolio of data products consumed across ecommerce, POS, B2B, and analytics. • Partner closely with Data Engineering to evolve the underlying data platform—including matching, classification, canonical naming, and quality—to deliver measurable customer and business outcomes. • Define and ship data-driven experiences for our customer bases: retailer-facing (e.g., operational efficiency), business-facing (e.g., content contribution via partner portals), and internal (e.g., APIs, SLAs). • Develop next-generation AI and Machine Learning systems, such as next best action and recommendations engines, to drive significant product improvements. • Own the architecture and governance models for critical data assets (e.g., product identifiers, taxonomies), resolving conflicts and balancing canonical authority with contributor flexibility. • Translate data coverage, quality, and structure into measurable business outcomes, such as merchandising conversion, receiving efficiency, or analytic integrity.