Internship- Co-Op: Data Engineer

ASML•San Diego, CA

1d•Hybrid

About The Position

As a Data Engineer Co-Op at ASML, you will help develop data pipelines and data products that turn laser-machine and enterprise data into trusted, usable information. Working with experienced data engineers, data architects, IT specialists, and business stakeholders, you will contribute technical solutions supporting product capabilities, analytics, AI, system performance, and customer satisfaction. The role offers experience with complex, high-value data and with how modern data solutions are designed, tested, deployed, documented, and operated at enterprise scale. As part of the CDO US team, you will develop and enhance data pipeline solutions; build ingestion, transformation, validation, and integration processes; and contribute to automated continuous data integration pipelines. You will develop and optimize SQL transformations, data models, and analytical datasets; help improve data quality, observability, monitoring, lineage, and governance; and refactor existing code as legacy and modern systems transition. You will collaborate with engineering and business teams, translate business requirements into technical solutions, and take part in code reviews, technical design discussions, agile delivery, and continuous improvement. You will also document data solutions and contribute reusable components and engineering patterns. The co-op is intended for a student pursuing a relevant bachelor's or master's degree. Success includes delivering working data pipelines, transformations, or data products; improving data quality and reliability; applying disciplined engineering practices; and collaborating effectively across data, IT, engineering, analytics, and AI teams.

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 code health, testing, maintainability, and documentation.

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 system performance improvements.
Understand business-user requirements and translate business 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 and improve existing code to support coexistence of legacy and modern systems during technology transitions.
Participate in code reviews, technical design discussions, agile delivery activities, 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.