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Vice President, Platform Architecture

mastercard · New York City

New
Senior 🇬🇧 English
AI/ML architecture Enterprise-scale ML platforms Feature stores Model training infrastructure Deployment pipelines Spark Airflow Delta Lake Kubernetes Service meshes APIs Security-by-design PCI DSS PII governance Design systems Micro-frontends

Job description

About the role

The Vice President, Enterprise Architecture will define and govern the long‑term architectural vision across the organization’s portfolio of AI, data, UX, and platform products. This leader ensures solutions are scalable, secure, resilient, and composable while aligning technology investments with business strategy.

Key responsibilities

  • Define and own the long‑term architecture strategy across AI, data, UX, and platform products, and lead the architecture review process.
  • Provide architectural oversight for the Commercial Data Fabric, AI workloads, and agentic platforms, ensuring reuse and preventing fragmentation.
  • Establish standards for APIs, service boundaries, deployment architectures, and embed security‑by‑design, compliance, and privacy requirements.
  • Serve as trusted advisor to senior executives on technology investments, vendor selection, and build‑versus‑buy decisions, creating multi‑year roadmaps.
  • Mentor principal architects and senior engineering leaders, fostering a culture of architectural excellence and continuous learning.

Required profile

  • 15+ years of software engineering experience with 8+ years in enterprise, platform, or systems architecture roles.
  • 5+ years of senior technology leadership managing architects and engineering leaders in global, matrixed organizations.
  • Proven track record developing and executing architecture strategies across complex, multi‑platform environments.
  • Experience in financial services, payments, fintech, or other highly regulated industries.
  • Demonstrated ability to influence executive stakeholders and drive decisions through technical leadership.

Required skills

  • AI/ML architecture and enterprise‑scale ML platforms
  • Feature stores, model training infrastructure, deployment pipelines, monitoring frameworks
  • Spark, Airflow, Delta Lake, Databricks DLT
  • Kubernetes, service meshes, APIs, micro‑services
  • Security‑by‑design, PCI DSS, PII governance
  • Design systems, component libraries, micro‑frontends

Questions fréquentes

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Source : ats:workday

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Published 3 days ago

Expires 1 month from now

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New York City