ASMLData Engineer
任职要求
Introduction to the job ASML Customer Support (CS) Diagnostics is at the core of ASML’s ambition to significantly reduce diagnostic labor hours, improve system availability, and enable predictive and self‑healing service capabilities towards 2030. The Data Engineering Engineer who will play a key role in building, scaling, and operationalizing AI‑driven diagnostics, observability, and predictive maintenance solutions. This role goes beyond tooling or automation: you will own the full lifecycle of data and AI solutions that directly impact diagnostic accuracy, MTTR, MTBF, and service efficiency. And you will work at the intersection of machine data, diagnostics domain knowledge, and advanced analytics, collaborating closely with CS Diagnostics, Field, D&E, and central platform teams Role and responsibilities AI, Analytics & Model Ownership Design, develop, deploy, and maintain machine learning and deep learning models for Predictive Maintenance (PdM), Fault Detection & Classification, and root‑cause identification and observability improvement. Own the end‑to‑end model lifecycle, problem definition and data exploration, feature engineering and model development, validation, deployment, monitoring, and retraining. Continuously improve model performance based on field feedback, diagnostic outcomes, and new data availability. Data Engineering & Platform Development Design and implement scalable, cloud‑native data pipelines to ingest, transform, and provision large volumes of structured and unstructured machine data. Work with platforms such as Azure, Databricks, Spark, and Kusto to ensure reliable, performant, and secure data access. Ensure data quality, traceability, and reproducibility for downstream analytics and AI applications. Enable early access to data through proof‑of‑concept pipelines, while ensuring smooth transition to production‑grade solutions Diagnostics Domain Enablement Improve observability through machine data by identifying gaps, defining required signals, and translating diagnostic needs into data and model …
工作职责
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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
• Own team-specific data pipelines and products end-to-end. • Plan, execute, and maintain data engineering roadmaps, aligning with wider company initiatives. • Define what data is collected to serve our evolving business needs. • Develop pipelines to deliver new datasets, uncover insights, and improve decision-making. • Continuously improve the scalability, reliability, and performance of our data systems. • Support data analysts and other stakeholders with timely, accurate data. • Participate in on-call rotations to maintain pipeline stability.
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.