Vice President, Enterprise Platforms

About the role

The Vice President, Enterprise Platforms leads enterprise architecture, client solutioning and pre-sales strategy for pharmaceutical and life sciences organizations. The role bridges sales opportunity ownership with hands-on technical leadership, partnering with Sales, Strategy, Medical, Media and Delivery teams to position technology as a differentiator in multi-million-dollar deals while setting scalable architecture standards.

Key responsibilities

• Lead enterprise pre-sales strategy, pipeline forecasting and opportunity management for multi-million-dollar pharmaceutical platform engagements with Sales and Account teams.
• Run executive discovery workshops and roadmap sessions with senior pharma stakeholders to define CRM, marketing automation and data architecture strategies.
• Design end-to-end omnichannel architectures covering Salesforce, Veeva, Adobe, Marketo, SFMC, Snowflake, Databricks, Fabric, CDPs, APIs and integration layers.
• Produce architecture diagrams, interaction models, data flows, solution specifications, pricing estimates and SOW documentation.
• Establish governance frameworks, architecture standards and AI-enabled solution patterns, guide steering committees and mentor internal architecture teams.

Required profile

• 10+ years in pharmaceutical or life sciences technology, consulting or enterprise sales operations, owning complex client engagements.
• 5+ years of hands-on enterprise architecture or solution design, with documented diagrams, data models and technical specifications.
• Proven track record leading pre-sales and managing pipelines for multi-million-dollar opportunities, including forecasting and proposals.
• Experience presenting technical strategies and roadmaps to senior stakeholders and steering committees.

Required skills

• Deep expertise in enterprise platforms such as Salesforce, Veeva, Adobe Experience Cloud, Marketo, SFMC, Snowflake, Databricks, Microsoft Fabric or similar ecosystems.
• Desired: comfort using AI regularly, experience with AI/ML applications such as next-best-action, personalization, predictive analytics or generative AI, and formal training in TOGAF, SAFe or equivalent frameworks.