Director Software Engineering - Data Engineering, Metadata management, Generative AI, DaaS, MaaS

Location Chennai, TN, India
Experience 10+ yrs
Job type Full-time
Posted 7 Oct 2026
Qualification Bachelor's

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You will be taken to American Express’s own careers page to apply. Job ID 26014517.

Job description

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.

Responsibilities

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.

Requirements

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.

Job description

Core Responsibilities

Servicing Data Foundation Enablement

Responsibilities

Requirements