MetLife Hiring! Full Time Senior Manager, Digital Analytics, Asia in Federal Territory - Ricebowl

Role Summary

The Digital Analytics Senior Manager/Manager is responsible for establishing and driving a robust digital analytics capability across regional and local markets. This role will define analytics governance, ensure data quality and consistency, develop actionable insights, and enable data-driven decision making through descriptive, diagnostic, predictive, and prescriptive analytics. The position will work closely with business stakeholders, customer experience teams, IT, data engineering, and market analytics teams to optimize customer experiences and business performance.

Key Responsibilities

1. Analytics Governance, Standards & Compliance

• Define and align digital analytics standards, KPIs, metrics, tracking frameworks, and dashboarding requirements across markets.
• Establish and maintain governance frameworks to ensure consistency, compliance, and scalability of analytics practices.
• Develop and facilitate operating models, including processes, RACI structures, escalation paths, and compliance monitoring.
• Conduct regular audits and compliance reviews to ensure adherence to analytics standards and data governance requirements.
• Drive alignment between regional and local stakeholders on analytics priorities and best practices.

2. Tracking Implementation & Data Quality Management

• Establish ownership and accountability for analytics tracking implementation and operations.
• Collaborate with market and regional IT teams, vendors, and product owners to design and implement tracking requirements across digital platforms.
• Partner with data engineers and analytics teams to align data definitions, formulas, taxonomies, and reporting properties.
• Ensure tracking accuracy and completeness across websites, mobile applications, and digital channels.
• Continuously monitor and improve data quality, focusing on accuracy, consistency, completeness, and reliability.

3. Reporting, Monitoring & Insights

• Consolidate market-level performance metrics into regional executive dashboards and reports.
• Design, develop, and maintain enterprise-level reporting solutions that provide a holistic view of digital performance.
• Monitor key performance indicators and proactively identify anomalies, threshold breaches, trends, and performance gaps.
• Translate complex datasets into meaningful visualizations and executive-ready reports.
• Deliver actionable insights and recommendations to stakeholders to improve customer experience and business outcomes.

4. Analytics Capability Building

• Develop analytics capabilities across regional and local teams through training, coaching, and knowledge-sharing initiatives.
• Promote adoption of analytics tools, methodologies, and best practices.
• Facilitate cross-functional collaboration and project-based learning between regional and local teams.
• Build analytics maturity and foster a culture of data-driven decision making across the organization.

5. Data Analysis & Optimization

• Conduct descriptive and diagnostic analyses to identify customer behavior trends, business issues, growth opportunities, and optimization opportunities.
• Work closely with Customer Experience Strategy, Product, and Design teams to support experimentation, A/B testing, and experience optimization initiatives.
• Translate analytical findings into strategic business recommendations and measurable actions.
• Develop customer segmentation and clustering frameworks to support targeting, personalization, and performance improvement initiatives.
• Measure and evaluate the impact of optimization and customer experience programs.

6. Data Intelligence & Advanced Analytics

• Work with engineers to develop predictive models and propensity models to understand customer preferences, behaviors, and likelihood of conversion or engagement.
• Generate prescriptive recommendations that support experimentation and optimization strategies.
• Identify opportunities to enrich customer, behavioral, and operational datasets from multiple internal and external sources.
• Support advanced analytics initiatives that improve customer experience, business performance, and operational effectiveness.

7. AI & Automation

• Identify and implement AI and automation opportunities that improve analytics efficiency and reduce manual effort.
• Drive the adoption of automated reporting, anomaly detection, and insight-generation capabilities.
• Evaluate emerging analytics, machine learning, and AI technologies to enhance business value.
• Promote data and analytics innovation across the organization.

Qualifications & Experience

Required

• Bachelor's degree in Analytics, Statistics, Data Science, Computer Science, Business, Marketing, or a related field.
• 7+ years of experience in digital analytics, customer analytics, business intelligence, or data analysis.
• Proven experience working with enterprise analytics platforms such as Adobe Analytics, Google Analytics 4 (GA4), Adobe Customer Journey Analytics, or similar tools.
• Strong understanding of digital tracking methodologies, tagging frameworks, and data governance.
• Experience creating executive dashboards and reporting solutions using tools such as Power BI, Tableau, Looker, or similar.
• Strong stakeholder management and cross-functional collaboration skills.
• Excellent analytical thinking, problem-solving, and communication skills.

Preferred

• Experience managing regional or multi-market analytics programs.
• Knowledge of customer experience, digital product, and marketing analytics.
• Experience with predictive analytics, machine learning, and customer segmentation techniques.
• Familiarity with SQL, Python, R, cloud data platforms, and customer data platforms (CDPs).
• Experience leading analytics capability-building initiatives and communities of practice.

Success Measures

• Adoption and compliance with analytics standards across markets.
• Improvement in tracking accuracy, completeness, and data quality.
• Timely delivery of executive dashboards and actionable insights.
• Increased usage of analytics in business decision making.
• Successful implementation of predictive analytics and AI-enabled use cases.
• Measurable improvements in customer experience and digital performance outcomes.
• Reduction in manual reporting efforts through automation and process optimization.