{"id":1986850,"url":"https://alion.io/job/american-express-director-software-engineering-data-engineering-metadata-management-generative-ai-daas-maas","title":"Director Software Engineering - Data Engineering, Metadata management, Generative AI, DaaS, MaaS","company":{"id":2209,"name":"American Express","domain":"americanexpress.com","url":"https://alion.io/company/american-express","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":99,"open_postings":47,"ghost_share":0,"stale_share":0.085,"repost_share":0.277,"time_to_fill_p50_days":8,"computed_at":"2026-10-27T05:46:45Z"}},"role":"Leadership","role_family":"Leadership","seniority":"head","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":48000,"max_usd":99000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":50},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"Machine Learning","optional":false},{"name":"Master Data Management","optional":false},{"name":"Platform Engineering","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false}],"status":"closed","first_seen_at":"2026-10-07T01:54:15Z","employer_posted_date":"2026-10-07","last_verified_at":"2026-10-26T21:23:49Z","board_verified":false,"closed_at":"2026-10-26T21:23:49Z","days_open":19,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":19},"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.\nCore Responsibilities\nData Platform Strategy, Modernization & Engineering Execution\nOwn 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.\nDefine and execute multi-year platform modernization strategies aligned to cloud-first, AI-enabled enterprise objectives.\nDrive platform simplification, standardization, and reuse by reducing redundant data assets, duplicate metrics, and fragmented servicing capabilities.\nEstablish platform scalability, resiliency, availability, and performance objectives while ensuring operational excellence.\nLead engineering teams responsible for platform development, reliability, automation, and lifecycle management.\nServicing Data Foundation Enablement\nBuild and evolve foundational data capabilities that make data trusted, accessible, reusable, and consumable across servicing domains.\nEstablish enterprise patterns for data federation, metadata management, lineage, cataloging, context propagation, and reusable intelligence services.\nEnable real-time and near-real-time access to servicing data through governed APIs, event-driven architectures, and intelligence access layers.\nDrive adoption of Data as a Service and Metrics as a Service capabilities across business and technology organizations.\nPartner with business stakeholders to ensure platform capabilities directly support servicing outcomes, customer experience improvements, and operational efficiency.\nServicing Data Governance, Risk, Security & Compliance\nEnsure governance, privacy, security, and compliance requirements are embedded into platform architecture, engineering processes, and operational practices.\nEstablish accountability for data quality, lineage, metadata management, retention, auditability, and access controls across servicing data assets.\nPartner with Enterprise Architecture, Information Security, Risk, Compliance, and Data Governance organizations to ensure alignment with enterprise standards.\nDrive proactive management of data risks and platform controls while enabling responsible innovation and faster delivery.\nAI, Intelligence Platforms & Reusable Services\nLead the development of reusable intelligence components and foundational services for servicing that accelerate AI, analytics, automation, and customer intelligence use cases.\nPartner 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.\nEnsure servicing data platforms are AI-ready by enabling high-quality, governed, and discoverable data assets.\nDrive reuse of data driven intelligence capabilities across servicing products and channels to maximize business value and reduce technology duplication.\nEvaluate emerging AI and data technologies and establish adoption strategies aligned to enterprise architecture, governance, and business priorities.\nOperational Insights, Reporting\nOwn the servicing operational reporting ecosystem, ensuring timely, accurate, and actionable insights are available to leaders, operations teams, and servicing platforms.\nMaximize the adoption of self service reporting and metric standardization across servicing and maintain minimal data products for servicing \nDrive modernization of operational reporting capabilities through real-time intelligence, self-service analytics, and reusable reporting frameworks.\nEstablish consistent metrics, KPIs, and measurement frameworks across servicing functions.\nPartner with business and operational leaders to translate data into actionable decisions and measurable business outcomes.\nEngineering Leadership, Talent & Organizational Excellence\nBuild and lead a high-performing engineering organization focused on platform engineering, operational excellence, innovation, and customer outcomes.\nDevelop engineering talent through coaching, mentoring, succession planning, and technical leadership development.\nFoster a culture of accountability, collaboration, continuous learning, and engineering excellence.\nEstablish engineering operating models, delivery practices, and quality standards that improve speed, reliability, and platform adoption.\nLead cross-functional partnerships across Product, Technology, Enterprise Architecture, Operations, Risk, and Vendor organizations.\nResponsibilities:-\nProven experience leading enterprise-scale data platform engineering organizations, with end-to-end ownership of strategy, architecture, execution, and operational excellence. \nDeep expertise in designing, building, and modernizing cloud-native data platforms, distributed systems, and large-scale data ecosystems supporting mission-critical business operations. \nDemonstrated success delivering multi-year platform modernization and cloud transformation initiatives, driving simplification, standardization, scalability, resilience, and platform reuse. \nExperience 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. \nStrong expertise in data engineering, metadata management, data quality, lineage, cataloging, governance, master data management, and enterprise data lifecycle management. \nDeep understanding of real-time data processing, event-driven architectures, streaming platforms, APIs, and operational reporting platforms that enable timely, trusted, and actionable business insights. \nExperience 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. \nStrong understanding of AI/ML ecosystems, including vector databases, Retrieval-Augmented Generation (RAG), semantic search, knowledge management, context engineering, and reusable AI platform services. \nProven ability to embed security, privacy, risk management, compliance, and governance into data platform architecture and engineering practices while meeting enterprise and regulatory requirements. \nDemonstrated ability to partner with Product, Technology, Data Science, Enterprise Architecture, Operations, Risk, and Business leaders to define strategic roadmaps and deliver measurable business outcomes. \nExceptional executive presence with a proven track record of influencing senior leadership, driving enterprise-wide platform adoption, and translating complex technical strategies into business value. \nStrong 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.\n Qualifications\nBachelor’s degree in Computer Science, Computer Engineering, Information Systems, or a related technical discipline; advanced degree preferred.\n10+ years of progressive software engineering and technology experience, including significant experience leading large-scale engineering teams and enterprise technology platforms.\nProven experience leading enterprise-scale data platform engineering organizations, with end-to-end accountability for technology strategy, architecture, engineering execution, platform reliability, and operational excellence.\nDeep expertise in designing, building, and modernizing cloud-native data platforms, distributed systems, data lakes, and large-scale data ecosystems supporting mission-critical business capabilities.\nDemonstrated success defining and executing multi-year platform modernization and cloud transformation strategies, with a focus on simplification, standardization, scalability, resiliency, automation, and reuse.\nStrong 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.\nDeep 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.\nStrong understanding of real-time and near-real-time data architectures, event-driven systems, streaming technologies, APIs, distributed processing, and operational reporting platforms.\nExperience building enterprise-scale operational reporting and analytics capabilities, including standardized metrics and KPIs, self-service analytics, real-time intelligence, and reusable reporting frameworks.\nDemonstrated experience establishing AI-ready data foundations that enable advanced analytics, machine learning, Generative AI, customer intelligence, agentic systems, automation, and intelligent decisioning.\nStrong 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.\nProven 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.\nStrong 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.\nDemonstrated ability to establish engineering standards and operating models that improve software quality, delivery velocity, platform availability, resiliency, performance, observability, and operational effectiveness.\nProven 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.\nStrong business and technology acumen with the ability to translate complex data and engineering strategies into measurable customer, operational, and business outcomes.\nExceptional 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.\nProven people leadership experience, including building, developing, mentoring, and scaling high-performing engineering organizations; developing technical leaders; and supporting talent development and succession planning.\nDemonstrated ability to lead through influence across complex, matrixed organizations and drive adoption of common platforms, reusable capabilities, engineering standards, and enterprise technology strategies.\nStrong commitment to engineering excellence and continuous improvement, with demonstrated success fostering a culture of accountability, innovation, collaboration, customer focus, and operational 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