Website Analytics Architect

Job Summary

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, Life Science, Electronics, 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.

Responsibilities

• 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. Coordinate external vendor discussions related to analytics architecture, AI capabilities, and data integrations; collaborate with Adobe and other vendors to enhance tools/features and advocate for the business community.
• Global and cross-functional collaboration. Work across time zones with global teams; influence and mentor on AI best practices in analytics, data ethics, and data literacy. Partner with IT and business stakeholders to drive continuous improvement and adoption of AI-enabled analytics practices.

Qualifications, Experience & Skills

• Minimum 5+ years designing and implementing an analytics framework on AEM for multiple websites; minimum 10+ years across web technologies.
• Deep experience with AEM as a Cloud Service (AEMaaCS), Adobe Launch, and Adobe Marketing Cloud.
• Strong background in analytics platforms (Adobe Analytics, Google Analytics, Google Data Studio, Synthesio) and in overseeing complex data integrations.
• Proven track record delivering AI-enabled analytics deployments.
• Experience with AI/ML integration into analytics workflows (model deployment, monitoring, governance).
• Familiarity with AI/ML use cases for web analytics, customer journey optimization, forecasting, anomaly detection, and attribution.
• Multichannel and responsive web expertise; experience in global, multi-region deployments and remote/multi-time-zone teams.
• Solid SDLC experience (Agile and Waterfall) with strong collaboration across business and IT stakeholders.
• Excellent communication and stakeholder management skills; demonstrated ability to present complex concepts to non-technical audiences.
• Healthcare domain experience or exposure is highly desirable.
• Governance and ethics. Knowledge of data privacy (e.g., GDPR/CCPA), data quality, and AI ethics considerations (bias mitigation, explainability, auditability).
• Nice-to-have. Experience with AI governance frameworks and model lifecycle management.
• Familiarity with data visualization tools and self-service BI that leverage AI-assisted insights.
• Experience building AI-powered content personalization or A/B testing optimization pipelines.
• Knowledge of enterprise data catalogs, metadata management, and data lineage for AI/ML workflows.

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