Internship/Co-Op: Data Engineer

ASML•San Diego, CA

8d•Hybrid

About The Position

As a Data Engineer Co-Op on ASML’s CDO US team, you will help develop data pipelines and data products that transform laser-machine and enterprise data into trusted, usable information. Working with experienced data engineers, data architects, IT specialists, and business stakeholders, you will contribute to technical solutions supporting product capabilities, analytics, AI, system performance, and customer satisfaction. The role offers experience with how enterprise-scale data solutions are designed, tested, deployed, documented, and operated. You will develop data ingestion, transformation, validation, and integration processes; optimize SQL transformations and data models; and contribute to automated continuous data integration pipelines. You will collaborate across engineering and business teams, help improve data quality and governance, and apply practices for code quality, testing, version control, maintainability, and documentation. You will also help refactor existing code and create reusable components and patterns. The CDO US team works across organizational and technical boundaries to create scalable business and engineering value from data. The co-op is intended for a student who is curious, collaborative, systematic, accountable, adaptable, and motivated to learn in a highly technical, international environment.

Requirements

Must be pursuing a Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Data Engineering, Information Systems, Software Engineering, or a related field.
Knowledge of or experience with Python, SQL, Java, or a comparable programming language.
Knowledge of relational databases, data modeling, and data integration concepts.
Familiarity with Databricks, SQL Server, or other database and data-platform technologies.
Familiarity with Microsoft Azure or another cloud environment.
Exposure to ETL/ELT development, data pipelines, or analytical data processing.
Academic, personal, or professional project experience performing data engineering or similar work.
Strong numerical, analytical, and structured problem-solving skills.
Ability to decompose complex problems, clarify requirements, and propose feasible solutions.
Attention to detail and ability to complete assignments accurately.
Ownership and accountability for assigned work, including raising risks and following through on commitments.
Ability to work effectively in a demanding environment with changing priorities and workloads.
Open, clear, concise, and professional communication.
Ability to establish cooperative working relationships with colleagues, technical teams, and business stakeholders.
Willingness to seek feedback, learn new technologies, and continuously improve.
Understanding of the importance of code health, testing, maintainability, and documentation.
Ability to balance immediate delivery needs with scalable and reusable solutions.

Nice To Haves

Familiarity with Git, automated testing, CI/CD, or modern software-development practices.
Experience with data quality, pipeline monitoring, or data visualization.

Responsibilities

Develop and enhance data pipeline solutions supporting product features, technical roadmaps, analytics, and AI use cases.
Build data ingestion, transformation, validation, and integration processes for laser-machine and enterprise data.
Contribute to automated continuous data integration pipelines used to build, test, and release data solutions.
Develop and optimize SQL transformations, data models, and analytical datasets.
Identify root causes of technical issues, resolve them, and standardize reusable solutions.
Collaborate with engineering and business teams on system design changes and performance improvements.
Understand business-user requirements and translate goals into feasible technical solutions.
Collaborate with data architects, software engineers, AI engineers, and IT team members.
Help improve data quality, observability, monitoring, lineage, and governance.
Apply practices for code quality, automated testing, version control, maintainability, and technical documentation.
Refactor existing code to support coexistence of legacy and modern systems during technology transitions.
Participate in code reviews, technical design discussions, agile delivery, and continuous improvement.
Document data pipelines, interfaces, data models, decisions, dependencies, and operating procedures.
Contribute reusable data components and engineering patterns for multiple teams and use cases.