The Senior Associate, Content Data Analyst supports metadata tagging operations, taxonomy health, and content operations reporting across the content ecosystem. The role is anchored in metadata and operational analytics, with emphasis on content health, reusability, velocity, tagging quality, and operational performance.
This role enables brand teams with reliable data and insights while partnering with insights teams, CCE Ops, and content operations stakeholders. The analyst will help maintain source-of-truth metadata, monitor operational health, and improve reporting consistency across tagging, reusability, and workflow performance.
What This Role Enables
Primary orientation
Metadata, taxonomy, content health, reusability, and operational velocity.
Business enablement
Provide brand teams and partner teams with accurate data for insights, optimization, and decision support.
Operational partnerships
Work closely with metadata/tagging teams, insights teams, CCE Ops, and content operations to improve quality and efficiency.
Expected maturity
Operate with high confidence in metadata and operations analytics, with clear ownership of data checks, dashboards, and reporting cadence.
Key Responsibilities
Metadata and Tagging Analytics
• Extract, analyze, and validate metadata and tagging datasets from AEM and related content systems.
• Monitor taxonomy usage, tagging completeness, content health, reusability, and metadata consistency across assets.
• Identify tagging gaps, taxonomy issues, and operational patterns that impact discoverability, reuse, and reporting reliability.
• Support tagging audits, quality reviews, and continuous improvement actions through structured analytics and clear reporting.
CCE Ops Performance and Reporting
• Develop and maintain operational dashboards covering throughput, turnaround time, backlog, handoffs, and efficiency metrics.
• Provide regular reporting on tagging and CCE Ops performance, highlighting bottlenecks, risks, and optimization opportunities.
• Partner with CCE Ops to translate operational questions into clear metrics and reporting outputs.
Tools and Systems
• Use Power BI, Qlik Sense, Excel, SQL, and basic Python to build, validate, and maintain reporting assets.
• Work with AEM, Adobe content ecosystem tools, Claravine, and related source data to support metadata and operational reporting.
• Document reporting logic, data sources, and operational handoffs to ensure repeatability and knowledge continuity.
Measurement, Data Quality and Visualization
• Build clean, structured datasets and visual reports that support operational decision-making and content performance visibility.
• Establish data quality checks, validation logic, and metric definitions to improve reporting accuracy and consistency.
• Identify opportunities to automate recurring reports and reduce manual effort across reporting cycles.
Stakeholder Collaboration
• Partner with India-based content production and tagging teams to support operational reporting needs.
• Collaborate with U.S. metadata, brand, insights, and content strategy partners to align on reporting priorities.
• Communicate findings in a concise, practical manner to support operational improvements and stakeholder decisions.
Not specified
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