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.
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.
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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.
Who you are (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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