Lead Architect: Enterprise Architecture

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As a Lead Architect at JPMorganChase within Corporate Technology with in the Corporate Data Analytics, you will define and deliver the target-state architecture for enterprise data interoperability and consumption across modern data platforms and on‑premises sources. You will set secure, reusable integration patterns for vendor data consumption with a focus on artificial intelligence and business intelligence use cases, aligned to cybersecurity, identity and access controls, and architecture governance. You will partner closely with engineering, security, and governance teams to turn architectural standards into shipped capabilities that scale across multiple lines of business.
Job responsibilities
Define and continuously evolve an inter-lake interoperability architecture blueprint spanning Databricks, Snowflake, enterprise data lake platforms, and on‑premises sources.
Produce hands-on architecture deliverables, including reference architectures, sequence and flow diagrams, decision records, and non-functional requirements.
Establish secure and reusable patterns for data movement and access, including batch, streaming, and change data capture where applicable.
Design vendor data consumption integration patterns into artificial intelligence and business intelligence platforms, focusing on usability, repeatability, and control alignment.
Specify authentication and authorization models that enforce least privilege, clear access boundaries, and auditable entitlements.
Define platform onboarding requirements, including security controls, governance readiness, operational support, service-level objectives, and monitoring expectations.
Lead assessments of new platform and product capabilities for internal and external data consumption applications, documenting trade-offs, risks, and remediation actions.
Facilitate Architecture Review Board intake, reviews, decisions, and action tracking to closure, maintaining reusable artifacts and outcomes.
Build and maintain knowledge assets such as playbooks, onboarding guides, templates, decision trees, and frequently asked questions to accelerate adoption.
Required qualifications, capabilities, and skills
Formal training or certification on architecture concepts and 5+ years applied experience
Eight years of experience in solution architecture, data architecture, or platform architecture, owning end-to-end target-state designs and delivery outcomes.
Hands-on architecture experience with Databricks and/or Snowflake, including interoperability and integration with enterprise data lake or lakehouse ecosystems.
Demonstrated expertise designing secure enterprise integrations, including authentication, authorization, encryption, audit logging, and monitoring.
Proven ability to define reference architectures, reusable patterns, and onboarding standards that are adopted by engineering teams.
Experience leading architecture governance processes and influencing decisions across engineering, cybersecurity, identity and access management, governance, and line-of-business stakeholders.
Ability to translate complex technical topics into clear, actionable guidance for both senior stakeholders and engineers.
Experience designing for scalability, resiliency, performance, and cost, including documenting measurable non-functional requirements.
Preferred qualifications, capabilities, and skills
Experience enabling artificial intelligence, machine learning, and business intelligence consumption at scale, including data serving patterns, semantic layers, or consumption APIs.
Familiarity with data governance capabilities such as metadata management, lineage, data classification, retention, and auditability.
Experience building standardized self-service onboarding experiences and reusable components for data consumers.
Experience with vendor data integration and third-party risk or security considerations in regulated environments.
Background partnering with platform teams to deliver reusable services that enable cross-domain data sharing with consistent guardrails.

Skills
Snowflake, Databricks, Machine Learning, Artificial Intelligence, Data Analysis, Onboarding