蚂蚁金服Ant International-Data Engineer - AML-US Risk Management
任职要求
Required Qualifications 1. MS degree in a technical field 2. 5 years of software engineering experience, end-to-end process ownership and customer obsession. 3. Proven expertise in any or all of the programming languages Java, SQL, Scala and related technology stacks. 4. Advan…
工作职责
Ant International serves customers around the world, and we are dedicated to providing safe and reliable risk control capabilities behind payments. The core technologies include rule engines, model engines, intelligent algorithm models, etc., involving very high concurrent real-time risk calculations and massive big data analysis and processing. It adopts a multi-center deployment architecture around the world. You are welcome to build it together. Here you may have the opportunity to learn more about and participate in the design and development of the following aspects: 1. Ultimate computing optimization at the millisecond level. 2. Behavior analysis and risk mining under massive data. 3. Global multi-center system architecture planning and high-availability solution design. You will also have the opportunity to explore the architectural design and implementation of cutting-edge technologies such as privacy computing and large models in AML systems. Welcome you to meet the challenge. Responsibilities 1. Build data pipelines that clean, transform, and aggregate data from disparate sources 2. Work closely with our business analysis team to provide unique insights into our data 3. Build robust systems for data quality assurance and validation at scale 4. Partner with data analysts on refining the data model used for reporting and analytical purposes
1. 负责国际业务数据产品的数据开发与架构设计，依据业务发展需求，构建高效、可靠的数据处理流程与数据模型，支撑业务分析与决策； 2. 参与数据平台核心模块的开发与优化，确保数据管道的稳定性、可扩展性与性能，通过技术手段解决大规模数据处理中的业务痛点； 3. 跟踪数据技术领域的前沿进展，负责将先进的数据处理技术与架构理念应用于实际项目，推动数据产品与业务流程的持续优化与创新； 4. 与跨部门团队紧密协作，深入理解业务需求，确保数据产品研发项目顺利推进，达成业务目标； 5. 负责数据开发相关技术规范的制定与流程的完善，提升团队技术输出的规范性与质量； 6. 参与数据产品研发团队的技术分享与知识沉淀，助力团队整体专业能力的提升; 7. 日常在英国伦敦办公室工作。
The Role： We are looking for a Data Engineer to be part of our Data Analytics team. This person will design, develop, maintain and support our Enterprise Data Warehouse & Manufacturing and Supply Chain Intelligent Solution within Tesla using various data & AI/BI tools, this position offers unique opportunity to make significant impact to the entire organization in developing data tools and applying AI into the process of manufacturing and supply chain lifecycle. Responsibilities: - Work in a time constrained environment to analyze, design, develop and deliver Enterprise Data Warehouse solutions for Supply Chain/Enterprise Teams. - Initiate or generalize BI solution across the system used by factories in different region globally - Factorize and translate business pain point into executable IT solutions - Setting up, maintaining and optimizing bigdata platform for production usage in reporting, analysis applications. - Establish scalable, efficient, automated processes for data analyses, model development, validation and implementation. - Create ETL pipelines using Spark/Flink. - Create real time data streaming and processing using technologies like Kafka , Spark etc. - Develop collaborative relationships with key business sponsors and IT resources for the efficient resolution of work requests. - Provide timely and accurate estimates for newly proposed functionality enhancements, especially in critical situations. - Develop, enforce, and recommend enhancements to Applications in the area of standards, methodologies, compliance, and quality assurance practices; participate in design and code walkthroughs. Minimum
Design, build, and manage cloud-based data warehouses and data access layers that support teams across Apple. Develop highly scalable and secure ETL pipelines to ingest, process, and serve data from a multitude of source systems. Perform data discovery and in-depth analysis across large, multi-source datasets, quickly learning new data to uncover insights and validate quality. Build proofs of concept and run ad-hoc and recurring analyses to answer high-priority business questions. Analyze and optimize existing data systems and queries, including Spark and Trino, for reliability, performance, and cost at scale. Use modern AI tools to enhance and automate data workflows such as orchestration, scheduling, and monitoring. Collaborate closely with US-based and regional teams and business partners, presenting findings and recommendations in English.