Data Engineer with DevOps experience,



Office Address

Project Description

ASML is one of the world's leading manufacturers of semiconductor-chip-making equipment. A majority of the world's microchips receive their critical lithographic patterning in machines made by
ASML. In addition ASML produces metrology tools and advanced applications to analyze and optimize the performance of the customer production process. At Business Line Applications, they develop
Analytics & Control solutions that improve the accuracy of performance metrics (such as overlay, focus, critical dimension) as measured on the end product of a fab process (wafers with chip structures).

We are looking for an experienced Data Engineer to join the Analytics & Visualization team. We are interested in engineers with strong knowledge and experience in building Data Intensive Applications (DIA's) in an industrial/enterprise environment. You are expected to handle data from various data sources and determine how best to structure the data in order to provide data in a ready-to-use form to data analysts that are looking to run queries and algorithms against the information for predictive & prescriptive analytics through machine learning. You will design and implement robust scalable data pipelines on the ecosystems existing in ASML and at our customers. You are expected to write high quality, maintainable, and robust code in Java, and provide solutions for ensuring long-term quality and integrity of the data.

The vacancy aims at supporting the development of novel data analytics, visualizations and control solutions by cutting edge techniques from Machine Learning and Data Mining. Successful methodologies aim to be included in ASML customer applications.


    • Design and implement Data Intensive Application's, realizing the product backlog defined by the Product Owner
    • Ensure quality of own deliverables, this includes designing and implementing automated tests (a.o. on unit- and integration levels)
    • Cooperate with other teams to ensure consistent implementation of the architecture, agree on interfaces and timing of cross-team deliveries
    • Troubleshoot, analyze and solve integration issues, both from internal alpha and beta tests, as well as reported by our customers
    • Write or update product documentation in accordance with company processes
    • Suggest improvements to our technical solutions and way of working, and implement them in alignment with your team and their stakeholders


Must have

    • M.Sc. or Ph.D. in Data Science, Computer Science, Electrical Engineering, Mathematics or Physics
    • Experience in having build DIA's in an industrial/enterprise environment
    • Experienced in software development (strong coding and testing skills) especially in Java
    • IT related knowledge is considered a must
    • Familiar languages include Java, Python etc.
    • Familiarity with relational and non-relational (documen, columnar, graph) database architectures Experience with frameworks from the big data ecosystems (Spark, Kafka, HBase, etc)
    • Experience with orchestration & containerization is a plus (Kubernetes, Docker, Mesos DC/OS)
    • Strong ability to communicate and negotiate designs of data pipelines with Data Architects and Data Scientists

Nice to have

    • Enthusiastic and intrinsically motivated, creative thinker
    • Good & proactive communication in an international and multidisciplinary environment
    • Taking responsibility, self-propelling
    • Goal-oriented and flexible mindset, willing to acquire lithography and other semiconductor manufacturing knowledge
    • Experience working on practical applications using real-world datasets
    • Hands-on with different data formats (CSV, XML, ARVO, TXT, JSON, etc.)
    • Familiarity with statistical languages like R and/or Matlab
    • Handle, analyze and visualize complex, high-volume, high-dimensional data from varying sources


English: B2 Upper Intermediate



Relocation package

If needed, we can help you with relocation process. Click here for more information.

Work Type

Data Modeling

Ref Number