Website Analytics Architect

The ideal candidate will architect, design, implement, and maintain a robust analytics solution for the global Adobe AEM Platform, with a strong emphasis on AI-driven insights and automation.

This role defines and operationalizes an AI-enabled analytics reporting framework to guide business decisions and optimization across Healthcare and Corporate websites (350+ sites) and multiple tools/integrations with Marketing & Sales systems.

You will partner with business, IT, and data governance stakeholders to deliver scalable, compliant, and secure analytics at scale, globally.

• Analytics strategy and design
• Define and continually enhance an AI-enabled analytics solution design across key digital platforms, with a focus on AI-assisted data discovery, anomaly detection, and prescriptive insights.
• Translate business and user needs into robust platform architectures that integrate AEM (including AEMaaCS), Adobe Launch, Adobe Analytics, Google Analytics, and AI/ML-driven BI solutions.
• AI-driven data integration and automation
• Lead data integration activities related to Adobe Launch, Adobe Analytics, Google Analytics, and AI-enabled data products (e.g., automated tagging, intelligent event sampling, auto-classification).
• Design and implement automated data pipelines, feature stores, and self-serve analytics templates that accelerate time-to-insight while ensuring data quality and governance.
• AI insights and analytics capabilities
• Develop and operationalize AI/ML models to enhance analytics outputs: anomaly detection, forecasting, customer journey optimization, sentiment/brand analytics, and multi-touch attribution.
• Implement AI-assisted tagging, content performance scoring, and anomaly alerts within the analytics stack.
• Reporting, visualization, and storytelling
• Oversee analytics reporting processes for digital marketing websites and develop scalable, AI-enabled reporting solutions and a sustainable support framework for the global organization.
• Manage analytics-related documentation and ensure adherence to data privacy, governance, and compliance guidelines; establish AI fairness, bias mitigation, and explainability practices for AI components.