This section describes the primary /essential responsibilities that this job performs. 1.Develop and manage the integration of data -- orchestration, ingestion, storage, format, transport, pipelines, and provisioning -- leveraging AI-driven automation for enhanced efficiency and accuracy. Make decisions about the selection of tools and technology. 2.Create and modify functions, programs, routines and procedures to export, transform and load data. Work across teams to optimize and build for re-use. 3.Develop logical and physical data models, applying AI/ML techniques for automated schema evolution, data normalization, and predictive data structuring. 4.Work with IT, business and data management teams to understand data needs and develop specialized solutions, utilizing AI-powered insights for real-time decision-making and predictive analytics 5.Collaborate with application integration and development teams on the selection and development of application data structures, storage, and integration in accordance with enterprise architectural standards. Serve as the liaison to application and integration teams. Learns the organizations’ enterprise applications, systems, and database portfolio, providing subject matter expertise to the team. 6.Creation and maintenance of RESTful APIs to expose access to Data. 7.Design for data quality, flexibility, and supportability, integrating AI-based data validation, auto-tagging, and metadata enrichment for improved governance. 8.Protect the organization and its assets with a strong consistent security awareness. 9.Optimize data integrations, balancing cloud and maintenance costs with performance. 10.Track industry changes and recommend improvements in data integration and architecture, including the adoption of AI/ML advancements for real-time analytics, automated feature engineering, and self-healing data pipelines. 11.Lead DevOps principles and agile development practices on the team to increase quality and reliability of software delivery, conduct code reviews and pull requests when needed 12.Use DevOps principles and agile development practices to increase quality and reliability of software delivery. Code review and pull requests. 13.Build automated tests and test datasets for repeatable software quality assurance testing. 14.Maintain reference documentation. 15.Demonstrate the Company’s Core and Growth Values in the performance of all job functions, while embracing AI innovation to drive efficiency, accuracy, and data-driven decision-making 主要职责： 1.开发和管理数据集成——包括编排、摄取、存储、格式、传输、管道和供应——利用 AI 驱动的自动化提升效率和准确性。就工具和技术的选择做出决策。 2.创建和修改函数、程序、例程和过程，以导出、转换和加载数据。跨团队协作以优化和构建可复用的解决方案。 3.开发逻辑和物理数据模型，应用 AI/ML 技术实现自动化模式演化、数据规范化和预测性数据结构。 4.与 IT、业务和数据管理团队合作，了解数据需求并开发专用解决方案，利用 AI 驱动的洞察力进行实时决策和预测分析。 5.与应用程序集成和开发团队合作，根据企业架构标准选择并开发应用程序数据结构、存储和集成。担任应用程序和集成团队的联络人。学习组织的企业应用、系统和数据库组合，为团队提供主题专业知识。 6.创建和维护 RESTful API 以提供数据访问接口。 7.为数据质量、灵活性和可支持性而设计，集成基于 AI 的数据验证、自动标记和元数据丰富以改进治理。 8.以强烈一致的安全意识保护组织及其资产。 9.优化数据集成，在云和维护成本与性能之间取得平衡。 10.跟踪行业变化，并就数据集成和架构提出改进建议，包括采用 AI/ML 进展来实现实时分析、自动化特征工程和自修复数据管道。 11.在团队中领导 DevOps 原则和敏捷开发实践，以提高软件交付的质量和可靠性，需要时进行代码审查和拉取请求（Pull Requests）。 12.使用 DevOps 原则和敏捷开发实践来提高软件交付的质量和可靠性。进行代码审查和拉取请求。 13.构建自动化测试和测试数据集，用于可重复的软件质量保证测试。 14.维护参考文档。 15.在所有工作职能履行中展现公司的核心价值和成长价值，同时拥抱 AI 创新以推动效率、准确性和数据驱动的决策。 This section describes the secondary responsibilities that this job performs. 1.Maintain reliable attendance. 2.Actively participate in departmental meetings, training and education. Assist with training other employees and providing backup. 3.Complete other assignments and special projects as requested. 次要工作职责： 1.保持可靠的出勤率。 2.积极参与部门会议、培训和教育。协助培训其他员工并提供后备支持。 3.按要求完成其他分配的任务和特殊项目。

Bachelor's degree in Computer Science or related field, Required 1.计算机科学或相关领域学士学位（必须） 8+ years' experience in data integration, data warehouse, and/or big data development, incorporating data architecture patterns and standards, as well as data integration design principles, working on an agile team, Required 2.8年以上数据集成、数据仓库和/或大数据开发经验，包含数据架构模式与标准以及数据集成设计原则，在敏捷团队中工作（必须） Azure cloud certified, Preferred 3.有Azure 云认证优先 Knowledge of various architectures, patterns, and protocols, such as data virtualization, UDM, CQRS, CDM, data lake, data warehouse, multi-cloud Hands-on experience with data fabric pattern or knowledge of hybrid integration platforms and application integration (APIs, RPC, SOA, messaging) Expert .net knowledge and experience Expert SQL knowledge, knowledge of NoSQL types, time-series, ledger, other immerging types Expertise in at least one big data programming language, Java preferred Understand agile data modeling, lineage, other metadata management Knowledge of data ingestion techniques, including streaming, synchronization, real-time, near real-time and mini-batch Knowledge of data management practices, including master data management, data governance and data product management Knowledge of and experience with data integration platforms, Microsoft Azure preferred Conversant in basic statistical and machine learning concepts, to collaborate effectively with data scientists and analysts Adaptability, learn and adopt new technology Advanced analytical and problem-solving skills Strong attention to detail Excellent verbal and written communication skills Ability to perform under strong demands in a fast-paced environment Effective time management and organizational skills Work independently as well as in a team environment Maintain confidentiality 4.了解各种架构、模式和协议，如数据虚拟化、统一维度模型 (UDM)、命令查询职责分离(CQRS)、通用数据模型 (CDM)、数据湖、数据仓库、多云架构。 5.具备数据编织模式 (data fabric pattern) 的实践经验，或了解混合集成平台和应用集成（API、RPC、SOA、消息传递）。 6.NET 专家级知识和经验。 7.SQL 专家级知识，了解 NoSQL 类型、时间序列、账本及其他新兴类型。 8.精通至少一种大数据编程语言，Java 优先。 9.理解敏捷数据建模、数据血缘关系 (lineage) 及其他元数据管理。 10.了解数据摄取技术，包括流式传输、同步、实时、近实时和小批量处理。 11.了解数据管理实践，包括主数据管理 (MDM)、数据治理和数据产品管理。 12.了解并具有数据集成平台经验，Microsoft Azure 优先。 13.熟悉基础统计和机器学习概念，以便与数据科学家和分析师有效协作。 14.适应性强，能够学习并采用新技术。 15.高级分析和解决问题的能力。 16.高度注重细节。 17.出色的口头和书面沟通能力。 18.能够在快节奏环境中承受高强度要求下高效工作。 19.有效的时间管理和组织能力。 20.既能独立工作，也能在团队环境中协作。 21.保守机密。 AI/Machine Learning Related Experience Expertise in at least one big data programming language (Java preferred) with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn). Experience with AI-driven ETL processes, including real-time data streaming, synchronization, and batch processing enhanced with machine learning. Understanding of AI-powered data ingestion techniques, such as automated feature extraction, anomaly detection, and predictive transformation pipelines. Knowledge of MLOps best practices, including continuous model deployment, monitoring, and retraining. Ability to collaborate with data science teams to embed machine learning models into data pipelines, ensuring scalability, reproducibility, and operational efficiency. AI/机器学习相关经验： - 精通至少一种大数据编程语言（Java优先）及 AI/ML 框架（如 TensorFlow, PyTorch, Scikit-Learn）。 - 具有 AI 驱动的 ETL 流程经验，包括利用机器学习增强的实时数据流、同步和批处理。 - 理解 AI 驱动的数据摄取技术，如自动化特征提取、异常检测和预测性转换管道。 - 了解 MLOps 最佳实践，包括持续模型部署、监控和再训练。 - 能够与数据科学团队合作，将机器学习模型嵌入数据管道，确保可扩展性、可重复性和操作效率。