Location: Oberkochen
Enabling smaller, more powerful and energy-efficient microchips – Working where tomorrow takes shape.
Around 80 percent of all microchips worldwide are manufactured using ZEISS technologies. As the heart of every electronically controlled system, they have become an indispensable part of our everyday lives – whether in smartphones, smart homes or smart factories. ZEISS is a technology leader in semiconductor manufacturing equipment. With high-precision lithography optics, photomask systems and process control solutions, ZEISS enables the production of ever smaller, more powerful and more energy-efficient microchips – and, through its innovations, plays a key role in shaping the age of micro- and nanoelectronics.
Your Role
Our team “Quality Assurance and Devops” within the department of Quality concerns itself with establishing quality assurance processes and DevOps standards for software applications and modules that are used in our production lines. The team develops and maintains platforms that support the implementation of core aspects of quality ranging from an effective software release and deployment platform to using data engineering approaches to build a reliability prediction platform for our production value streams.
Your role
- Reliability, Data Engineering and Full-Stack DevOps Engineer
- Develop a data-driven software reliability prediction platform for complex, production-relevant applications and machines
- Combine simulation, test, production, field issues, and DevOps data into scalable data pipelines and reliable analytical foundations.
- Apply statistical, data science, and machine learning methods to identify reliability risks, failure patterns and quality trends
- Design and implement full-stack web solutions, including user interfaces, backend services, APIs, dashboards, and integrations with enterprise systems that facilitate effective presentations of key learnings and trends towards stakeholders
- Integrate reliability models and algorithms into maintainable software components that support engineering and quality assurance decisions.
- Collaborate closely with operations and R&D to continue to build out and integrate effective Reliability prediction models that span across software and manufacturing machines
Your Profile
- Degree in computer science, data science, software engineering, industrial engineering, or a comparable technical field
- Strong software engineering skills with practical experience in Python, C#, TypeScript/JavaScript or any high-level programming languages as well as clean code, modular design, and maintainable architectures
- Hands on experience and understanding of data science, statistics, machine learning, predictive analytics and reliability modeling
- Experience with data engineering concepts such as data pipelines, data quality, ETL/ELT, databases, APIs, and heterogeneous technical data sources
- Hands-on experience in full-stack web development, including modern frontend frameworks, backend services, REST APIs, authentication concepts, dashboards, and visualizations
- Good knowledge of DevOps and cloud native development and deployment approaches, GitOps, CI/CD, automated testing, and agile software development practices
- Analytical mindset, structured working style, and the ability to translate complex technical requirements into pragmatic software solutions
- Comfortable with English. German is an added advantag
Your ZEISS Recruiting Team
Jonas Weickenmeier
Job description provided by Zeiss.
View the original posting
