[ About metlife ]Building financial security through trust and innovation.
MetLife is one of the world’s leading financial services companies, providing insurance, annuities, employee benefits, and asset management to more than 90 million customers across over 40 countries. Founded in 1868, MetLife has built its legacy on one enduring principle — helping people and institutions navigate life’s financial challenges with confidence and stability.
From protection to empowerment.
MetLife’s evolution reflects a shift from traditional insurance to integrated financial wellness. Through advanced analytics, digital platforms, and customer-centric design, the company empowers individuals, employers, and communities to plan, protect, and prosper in a changing world.
A legacy of trust and global resilience.
For over 150 years, MetLife’s purpose has been to help customers build a more confident future. Its commitment to reliability, transparency, and innovation continues to define how the company serves as a trusted partner in an increasingly connected and unpredictable global economy.
[ My Role ]At MetLife, I led the modernization of enterprise data intelligence—where analytics meets automation, and insight drives measurable value.
At MetLife, I directed enterprise-scale data transformation initiatives that redefined how information powered decisions across actuarial, claims, and fraud operations. My focus was on building the company’s next-generation data ecosystem—one capable of turning raw data into reliable, actionable insight while reinforcing transparency and trust across every line of business.
In this role, I led global data engineering and analytics programs that unified operations across finance, actuarial, and risk teams. By implementing a modern data lake architecture using Snowflake, Apache Airflow, and dbt, I accelerated data delivery cycles from 36 hours to under 3—improving operational responsiveness and decision accuracy. Collaborating with cross-functional partners, I drove the adoption of predictive modeling in Python, generating over $4.1M in annual fraud savings and advancing the organization’s data-driven culture.
Beyond infrastructure, I championed enterprise data literacy and governance, introducing data lineage standards that enhanced auditability and consistency enterprise-wide. These programs equipped teams with a shared language of data and accountability—ensuring that every insight not only informed the business but strengthened its resilience and confidence in every outcome.
[ strategic outcomes ]Strategic Outcomes
Enterprise Data Modernization
Directed the buildout of a global data lake architecture using Snowflake, Apache Airflow, and dbt—reducing BI latency from 36 hours to under 3 and enabling scalable, automated insight delivery across actuarial and risk domains.
Predictive Intelligence Enablement
Collaborated with actuarial and claims teams to deploy predictive modeling frameworks in Python—enhancing fraud detection accuracy and driving $4.1M in annual savings through proactive risk identification.
Analytics Democratization
Launched Tableau-based executive dashboards accessible to 700+ employees globally, transforming operational visibility and enabling real-time decision-making across finance, claims, and compliance functions.
Data Governance & Lineage
Established enterprise-wide data lineage and metadata standards, strengthening auditability, regulatory compliance, and trust in data-driven reporting across all business lines.
“John’s leadership redefined how data served the enterprise. He transformed fragmented information systems into a unified intelligence layer—turning data into a source of foresight, efficiency, and measurable impact. His calm precision and strategic vision helped MetLife make decisions not just faster, but smarter.”
— Elaine Porter, Vice President, Data Strategy & Analytics, MetLife
Data → Automation → Intelligence → Impact
Stage 01Enterprise Data Foundation
Built a global data lake ecosystem using Snowflake, Apache Airflow, and dbt—transforming fragmented data sources into a unified, automated analytics platform that powered faster and more reliable decision-making across actuarial, claims, and risk operations.
Stage 02Automated Insight Delivery
Automated BI pipelines and reporting workflows—reducing latency from 36 hours to under 3—while embedding governance and monitoring systems that ensured consistency, accuracy, and transparency across global datasets.
Stage 03Predictive Intelligence
Partnered with actuarial and fraud analytics teams to operationalize predictive models in Python—advancing proactive risk detection and generating $4.1M in annual savings through early fraud prevention and process optimization.
Stage 04Measured Impact
Delivered measurable gains in speed, precision, and organizational intelligence—expanding enterprise data access to 700+ employees, enhancing auditability, and positioning MetLife’s data ecosystem as a strategic driver of trust, efficiency, and growth.
[ the journey ]A disciplined ascent through data, automation, and trust.
Inclusions
At MetLife, my journey centered on transforming how data informs trust. From building a global data lake to deploying automated BI and predictive analytics, every initiative was driven by a balance of precision and progress—reducing latency, improving transparency, and strengthening enterprise-wide decision velocity. By uniting actuarial, claims, and risk analytics under a shared data framework, I helped establish a culture where intelligence served both scale and accountability.
This stage reinforced that transformation is not just about access to more data—it’s about empowering teams with reliable, interpretable insights. The goal was never to automate analysis for speed alone, but to design systems that made intelligence more human, measurable, and trusted across the enterprise.
Exclusions
At MetLife, I learned that innovation in data isn’t about volume or novelty—it’s about stewardship. Insight without structure creates noise. The focus was never on building the largest data system, but on building the most responsible one—where every algorithm, dashboard, and model reinforced confidence in the decisions they powered.