Data Engineer

Requirements

• Experience with diagnostics, manufacturing, equipment data, or industrial systems.
• Familiarity with ASML machine data and CS diagnostics workflows.
• Experience improving observability, fault detection, or predictive maintenance in complex systems.
• Experience working with business stakeholders and explaining technical results to non-technical audiences.
• Experience training others and creating technical documentation or user manuals.
• Inclusion and diversity
• N

Benefits

Additional Information

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 requirements. Identify structural improvements in diagnostic services, including processes, methods, and analytical approaches. Closely collaborate with diagnostics experts to ensure solutions are actionable, interpretable, and embedded in diagnostic workflows. Standards, Governance & Stakeholder Collaboration Define and follow standards, policies, and protocols for data, models, and analytics solutions. Ensure solutions are compliant, manageable, scalable, and secure, in line with ASML requirements. Translate technical outcomes into measurable service impact (e.g. MTTR reduction, labor hour savings, improved hit rates). Communicate results, insights, and recommendations to senior stakeholders and leadership. Provide guidance and knowledge sharing to colleagues and stakeholders where needed. Education, Experience and Skill Master's degree in Data Science, Computer Science, Engineering, Applied Mathematics, or a related field. 5+ years of relevant experience in data science, data engineering, or advanced analytics roles. Strong proficiency in Python and experience with analytical and ML libraries. Scripting skill such as PERL, Bash, Power Shell Proven experience developing and deploying machine learning / deep learning models in production environments. Strong experience with cloud‑based data platforms (Azure preferred), including Databricks, Spark, SQL / Kusto. Experience with SQL ETL processes. Solid understanding of statistics, data analysis, SPC/FDC concepts, and analytical problem solving. Experience working with large‑scale, high‑frequency data streams Other information

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