You strive to be an essential member of a diverse team of visionaries dedicated to making a lasting impact. Don’t pass up this opportunity to collaborate with some of the brightest minds in the field and deliver best-in-class solutions to the industry.你渴望成为多元化远见卓识团队中不可或缺的一员，并致力于产生持久的影响。千万不要错过这个与业内最聪明的大脑合作，并为行业提供一流解决方案的机会。

As a Senior Lead Data Architect, Data Modelling at JPMorganChase within Enterprise Platforms (Employee Platforms) in Corporate Technology, you shape how data is structured, defined, and trusted across products that serve critical employee experiences. You will lead the domain’s data modelling strategy across transactional, analytical, streaming, and AI-driven consumption patterns, ensuring performance, clarity, and safe evolution. You will also help establish semantic foundations so teams and enterprise-authorized AI capabilities can interpret and use data reliably with appropriate validation, security, resiliency, and auditability.作为摩根大通企业技术部企业平台（员工平台）的高级首席数据架构师（数据建模方向），你将负责塑造数据在服务于关键员工体验的产品中的结构、定义和信任方式。你将领导该领域跨事务、分析、流式处理和人工智能驱动消费模式的数据建模策略，确保其性能、清晰度和安全演进。你还将协助建立语义基础，以便团队和企业授权的人工智能能力能够通过适当的验证、安全、弹性和可审计性，可靠地解释和使用数据。

Job responsibilities工作职责

• Lead the end-to-end data modelling strategy for the domain, aligning logical and physical models to the needs of each product and consumer.领导该领域的端到端数据建模策略，使逻辑模型和物理模型与每个产品及消费者的需求保持一致。
• Design and optimize models across raw/denormalized document structures, OLTP relational (3NF), OLAP dimensional (star/snowflake), and event/message schemas.设计并优化跨原始/非规范化文档结构、OLTP 关系型（3NF）、OLAP 维度（星型/雪花型）以及事件/消息模式的模型。
• Define and maintain semantic foundations, including a data dictionary, naming and namespace conventions, and attribute-level abstractions to support interoperability.定义并维护语义基础，包括数据字典、命名和命名空间约定，以及支持互操作性的属性级抽象。
• Govern schema evolution and model versioning to enable safe change while protecting downstream consumers through clear contracts and lineage expectations.管理模式演进和模型版本控制，在通过清晰的契约和血缘预期保护下游消费者的同时，实现安全的变更。
• Represent data architecture and modelling at governance forums, improving standards and guiding technology evaluation against established frameworks.在治理论坛中代表数据架构和建模职能，改进标准并指导针对既定框架的技术评估。
• Provide technical direction and mentorship to engineering teams, contractors, and vendors as a domain subject matter expert in data modelling.作为数据建模领域的学科专家，为工程团队、承包商和供应商提供技术指导和指导。
• Develop and review secure, high-quality DDL and model-implementing transformation logic, debugging issues and improving production readiness.开发并审查安全、高质量的 DDL 和模型实现转换逻辑，调试问题并提高生产就绪水平。
• Drive modelling decisions that influence product design, application functionality, analytics outcomes, access-pattern performance, and operational stability.推动建模决策，从而影响产品设计、应用程序功能、分析结果、访问模式性能和运营稳定性。
• Use enterprise-authorized AI capabilities to accelerate modelling analysis and documentation while validating outputs and aligning to data sensitivity, security controls, resiliency, and auditability expectations.利用企业授权的人工智能能力来加速建模分析和文档编制，同时验证输出结果，并使其符合数据敏感性、安全控制、弹性和可审计性的预期。
• Champion reuse-first, AI-assisted validation and agentic workflows within the software development lifecycle to strengthen quality checks, documentation, traceability, and model usability for both people and AI systems.在软件开发生命周期中倡导优先重用、人工智能辅助验证和代理工作流，以加强质量检查、文档记录、可追溯性以及模型对人员和人工智能系统的可用性。

Required qualifications, capabilities and skills任职资格、能力和技能要求

• Formal training or certification on data architecture and data modelling concepts and 5+ years applied experience.接受过数据架构和数据建模概念的正规培训或认证，并具备 5 年以上应用经验。
• Extensive hands-on experience designing and delivering optimized data models across raw/document, OLTP 3NF, OLAP dimensional, and event/message schema patterns.在设计和交付跨原始/文档、OLTP 3NF、OLAP 维度以及事件/消息模式等优化数据模型方面拥有丰富的实践经验。
• Strong command of core data concepts, including entities, relationships, cardinality, normalization vs. denormalization trade-offs, attribute abstraction, and schema evolution.熟练掌握核心数据概念，包括实体、关系、基数、规范化与反规范化的权衡、属性抽象以及模式演进。
• Practical experience delivering system design, application development, testing, and operational stability in production environments.在生产环境中交付系统设计、应用程序开发、测试和运营稳定性方面的实践经验。
• Solid understanding of modern lakehouse and data and analytics platforms, including Databricks (Delta Lake, Unity Catalog, medallion architecture) or equivalent platforms.对现代湖仓一体 (Lakehouse) 以及数据和分析平台有扎实的理解，包括 Databricks（Delta Lake、Unity Catalog、奖章架构）或同等平台。
• Demonstrated experience using enterprise-authorized AI capabilities to support data modelling and architecture workflows, with strong validation habits and awareness of data sensitivity.具备使用企业授权的人工智能能力来支持数据建模和架构工作流的实践经验，具有良好的验证习惯和数据敏感性意识。
• Ability to assess and validate AI-assisted modelling recommendations before adoption, escalating uncertainty and ensuring alignment to security, auditability, and resiliency expectations.有能力在采用人工智能辅助的建模建议前对其进行评估和验证，上报不确定性，并确保其符合安全、可审计性和弹性的预期。
• Advanced knowledge in one or more programming languages and technical disciplines (e.g., cloud, AI/ML, data platforms), with strong architecture and engineering fundamentals.在一门或多门编程语言和技术学科（如云、人工智能/机器学习、数据平台）方面具备高级知识，并拥有扎实的架构和工程基础。
• Proven ability to independently solve complex data model design and functionality problems with little to no oversight.被证明有能力在几乎没有监督的情况下独立解决复杂的数据模型设计和功能问题。

Preferred qualifications, capabilities and skills优先考虑的资格、能力和技能

• Deep specialization in data modelling, including the ability to explain and teach model-shape decisions based on consumption patterns.在数据建模方面有深厚的专业造诣，包括能够根据消费模式解释和教授模型形态决策的能力。
• Experience defining and sustaining a shared business glossary and semantic layer that stays coherent across domains, including for AI-driven consumption.具备定义和维护跨领域保持一致的共享业务词汇表和语义层的经验，包括针对人工智能驱动的消费场景。
• Strong, current experience optimizing analytical/dimensional models on Databricks or comparable cloud-native data platforms, including performance and cost considerations at scale.在 Databricks 或同类云原生数据平台上优化分析/维度模型方面拥有扎实的最新经验，包括大规模性能和成本考量。
• Familiarity with vector and graph modelling and retrieval patterns for AI/ML and agentic consumption, in addition to traditional OLTP and OLAP modelling.除了传统的 OLTP 和 OLAP 建模外，还熟悉用于人工智能/机器学习和代理消费的向量和图建模及检索模式。
• Practical fluency integrating agentic AI workflows into modelling, documentation, and data quality processes, including effective prompting and rigorous validation.具备将代理人工智能工作流集成到建模、文档编制和数据质量流程中的实践能力，包括有效的提示词工程和严格的验证。
• Experience establishing or scaling modelling standards, naming conventions, and governance practices, including onboarding and mentoring other modelers and engineers.具备建立或扩展建模标准、命名约定和治理实践的经验，包括入职培训和指导其他建模人员及工程师。

• Exposure to schema/contract-driven development (e.g., data contracts, schema registries) and managing schema evolution safely in production.接触过模式/契约驱动开发（例如数据契约、模式注册表）并能安全地管理生产环境中的模式演进。