亚马逊Data Engineer II, ROW AOP
任职要求
基本任职资格 - Bachelor's degree or above in computer science, computer engineering, or related field, or Master's degree - 3+ years of data engineering experience - Knowledge of at least two of the following programming languages: Scala, Java, Python, C/C++, or Go - Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse - Knowledge of cloud services such as AWS or equivalent - Knowledge of data warehouse technical architecture, infrastructure components, ETL and reporting/analytic tools and environments - Experience with version control systems and CI/CD pipeline implementation - Experience in complex problem solving, and working in a tight schedule environment - Experience working in a collaborative team environment to deliver high-quality design solutions 优先任职资格 - Experience in industry, consulting, government or academic research - Experience wi…
工作职责
1. Design, develop, and maintain scalable data pipelines to support ML model development and production deployment. 2. Implement and maintain CI/CD pipelines for the data and ML solutions. 3. Collaborate with data scientists and other team members to understand data requirements and implement efficient data processing solutions. 4. Create and manage data warehouses and data lakes, ensuring proper data governance and security measures are in place. 5. Collaborate with product managers and business stakeholders to understand data needs and translate them into technical requirements. 6. Stay current with emerging technologies and best practices in data engineering, and propose innovative solutions to improve data infrastructure and processes for ML models and analytics applications. 7. Participate in code reviews and contribute to the development of best practices for data engineering within the team.
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.