Lead the strategy, engineering, and evolution of the Global Servicing Data Foundation and common/reusable Intelligence Access Layer, enabling trusted, governed, reusable, and AI-ready data capabilities at enterprise scale. Drive the modernization of servicing data platforms & reusable intelligence components that power operational insights, customer journey intelligence, analytics, automation, and AI-driven servicing experiences across American Express.
Core Responsibilities
Data Platform Strategy, Modernization & Engineering Execution
- Own the strategy, roadmap, and execution for Servicing Data Platforms, including Data as a Service (DaaS), Metrics as a Service (MaaS), Operational Data Engineering, Operational Reporting Platforms (real-time and historical), Customer Journey Intelligence Engine (CIE), Data Ingestion Platforms, and Data Lake Modernization.
- Define and execute multi-year platform modernization strategies aligned to cloud-first, AI-enabled enterprise objectives.
- Drive platform simplification, standardization, and reuse by reducing redundant data assets, duplicate metrics, and fragmented servicing capabilities.
- Establish platform scalability, resiliency, availability, and performance objectives while ensuring operational excellence.
- Lead engineering teams responsible for platform development, reliability, automation, and lifecycle management.
Servicing Data Foundation Enablement
- Build and evolve foundational data capabilities that make data trusted, accessible, reusable, and consumable across servicing domains.
- Establish enterprise patterns for data federation, metadata management, lineage, cataloging, context propagation, and reusable intelligence services.
- Enable real-time and near-real-time access to servicing data through governed APIs, event-driven architectures, and intelligence access layers.
- Drive adoption of Data as a Service and Metrics as a Service capabilities across business and technology organizations.
- Partner with business stakeholders to ensure platform capabilities directly support servicing outcomes, customer experience improvements, and operational efficiency.
Servicing Data Governance, Risk, Security & Compliance
- Ensure governance, privacy, security, and compliance requirements are embedded into platform architecture, engineering processes, and operational practices.
- Establish accountability for data quality, lineage, metadata management, retention, auditability, and access controls across servicing data assets.
- Partner with Enterprise Architecture, Information Security, Risk, Compliance, and Data Governance organizations to ensure alignment with enterprise standards.
- Drive proactive management of data risks and platform controls while enabling responsible innovation and faster delivery.
AI, Intelligence Platforms & Reusable Services
- Lead the development of reusable intelligence components and foundational services for servicing that accelerate AI, analytics, automation, and customer intelligence use cases.
- Partner with staff engineers, peer technology and product teams to establish scalable platforms that support predictive analytics, customer journey intelligence, GenAI, agentic systems, and decisioning capabilities.
- Ensure servicing data platforms are AI-ready by enabling high-quality, governed, and discoverable data assets.
- Drive reuse of data driven intelligence capabilities across servicing products and channels to maximize business value and reduce technology duplication.
- Evaluate emerging AI and data technologies and establish adoption strategies aligned to enterprise architecture, governance, and business priorities.
Operational Insights, Reporting
- Own the servicing operational reporting ecosystem, ensuring timely, accurate, and actionable insights are available to leaders, operations teams, and servicing platforms.
- Maximize the adoption of self service reporting and metric standardization across servicing and maintain minimal data products for servicing
- Drive modernization of operational reporting capabilities through real-time intelligence, self-service analytics, and reusable reporting frameworks.
- Establish consistent metrics, KPIs, and measurement frameworks across servicing functions.
- Partner with business and operational leaders to translate data into actionable decisions and measurable business outcomes.
Engineering Leadership, Talent & Organizational Excellence
- Build and lead a high-performing engineering organization focused on platform engineering, operational excellence, innovation, and customer outcomes.
- Develop engineering talent through coaching, mentoring, succession planning, and technical leadership development.
- Foster a culture of accountability, collaboration, continuous learning, and engineering excellence.
- Establish engineering operating models, delivery practices, and quality standards that improve speed, reliability, and platform adoption.
- Lead cross-functional partnerships across Product, Technology, Enterprise Architecture, Operations, Risk, and Vendor organizations.
Responsibilities:-
- Proven experience leading enterprise-scale data platform engineering organizations, with end-to-end ownership of strategy, architecture, execution, and operational excellence.
- Deep expertise in designing, building, and modernizing cloud-native data platforms, distributed systems, and large-scale data ecosystems supporting mission-critical business operations.
- Demonstrated success delivering multi-year platform modernization and cloud transformation initiatives, driving simplification, standardization, scalability, resilience, and platform reuse.
- Experience establishing and operating Data as a Service (DaaS), Metrics as a Service (MaaS), reusable data products, intelligence access layers, and API/event-driven data platforms.
- Strong expertise in data engineering, metadata management, data quality, lineage, cataloging, governance, master data management, and enterprise data lifecycle management.
- Deep understanding of real-time data processing, event-driven architectures, streaming platforms, APIs, and operational reporting platforms that enable timely, trusted, and actionable business insights.
- Experience building AI-ready data foundations that support advanced analytics, machine learning, GenAI, customer intelligence, agentic systems, and intelligent decisioning through governed, contextualized, and discoverable data assets.
- Strong understanding of AI/ML ecosystems, including vector databases, Retrieval-Augmented Generation (RAG), semantic search, knowledge management, context engineering, and reusable AI platform services.
- Proven ability to embed security, privacy, risk management, compliance, and governance into data platform architecture and engineering practices while meeting enterprise and regulatory requirements.
- Demonstrated ability to partner with Product, Technology, Data Science, Enterprise Architecture, Operations, Risk, and Business leaders to define strategic roadmaps and deliver measurable business outcomes.
- Exceptional executive presence with a proven track record of influencing senior leadership, driving enterprise-wide platform adoption, and translating complex technical strategies into business value.
- Strong people leadership experience with success in building, mentoring, and scaling high-performing engineering organizations while fostering a culture of innovation, accountability, operational excellence, and continuous learning.
Qualifications
- Bachelor’s degree in Computer Science, Computer Engineering, Information Systems, or a related technical discipline; advanced degree preferred.
- 10+ years of progressive software engineering and technology experience, including significant experience leading large-scale engineering teams and enterprise technology platforms.
- Proven experience leading enterprise-scale data platform engineering organizations, with end-to-end accountability for technology strategy, architecture, engineering execution, platform reliability, and operational excellence.
- Deep expertise in designing, building, and modernizing cloud-native data platforms, distributed systems, data lakes, and large-scale data ecosystems supporting mission-critical business capabilities.
- Demonstrated success defining and executing multi-year platform modernization and cloud transformation strategies, with a focus on simplification, standardization, scalability, resiliency, automation, and reuse.
- Strong experience developing and operating enterprise data capabilities such as Data as a Service (DaaS), Metrics as a Service (MaaS), reusable data products, intelligence access layers, data ingestion platforms, and governed APIs.
- Deep knowledge of modern data engineering principles and technologies, including data ingestion and processing, data federation, metadata management, data quality, lineage, cataloging, master data management, and enterprise data lifecycle management.
- Strong understanding of real-time and near-real-time data architectures, event-driven systems, streaming technologies, APIs, distributed processing, and operational reporting platforms.
- Experience building enterprise-scale operational reporting and analytics capabilities, including standardized metrics and KPIs, self-service analytics, real-time intelligence, and reusable reporting frameworks.
- Demonstrated experience establishing AI-ready data foundations that enable advanced analytics, machine learning, Generative AI, customer intelligence, agentic systems, automation, and intelligent decisioning.
- Strong understanding of modern AI/ML and Generative AI ecosystems, including large language models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, semantic search, knowledge management, context engineering, and reusable AI/intelligence services.
- Proven ability to evaluate emerging data, cloud, AI, and GenAI technologies and establish enterprise adoption strategies that balance innovation with architecture standards, scalability, security, governance, and business priorities.
- Strong knowledge of enterprise data governance, security, privacy, risk, and regulatory requirements, with demonstrated experience embedding data quality, access controls, lineage, retention, auditability, and governance into platform architecture and engineering practices.
- Demonstrated ability to establish engineering standards and operating models that improve software quality, delivery velocity, platform availability, resiliency, performance, observability, and operational effectiveness.
- Proven ability to partner effectively across Product, Technology, Data Science, Enterprise Architecture, Information Security, Risk, Compliance, Operations, and business organizations to define strategic roadmaps and deliver enterprise capabilities.
- Strong business and technology acumen with the ability to translate complex data and engineering strategies into measurable customer, operational, and business outcomes.
- Exceptional communication and executive-influencing skills, with demonstrated ability to communicate complex technology strategies, architecture decisions, investment priorities, risks, and trade-offs to senior technical and business leaders.
- Proven people leadership experience, including building, developing, mentoring, and scaling high-performing engineering organizations; developing technical leaders; and supporting talent development and succession planning.
- Demonstrated ability to lead through influence across complex, matrixed organizations and drive adoption of common platforms, reusable capabilities, engineering standards, and enterprise technology strategies.
- Strong commitment to engineering excellence and continuous improvement, with demonstrated success fostering a culture of accountability, innovation, collaboration, customer focus, and operational excellence.