Jobbeschreibung
Step out of your comfort zone, excel and redefine the limits of what is possible. That's just what our employees are doing every single day – in order to set the pace through our innovations and enable outstanding achievements. After all, behind every successful company are many great fascinating people.
In a spacious modern setting full of opportunities for further development, ZEISS employees work in a place where expert knowledge and team spirit reign supreme. All of this is supported by a special ownership structure and the long-term goal of the Carl Zeiss Foundation: to bring science and society into the future together.
Join us today. Inspire people tomorrow.
Diversity is a part of ZEISS. We look forward to receiving your application regardless of gender, nationality, ethnic and social origin, religion, philosophy of life, disability, age, sexual orientation or identity.
Apply now! It takes less than 10 minutes.
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Design and implement scalable, reliable, and efficient data architecture to support large-scale data processing and analytics needs
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Develop, maintain, and optimize data pipelines and ensure data quality, reliability, and timeliness for ingestion and processing. Ensure consistent code quality and adherence to technical guidelines across the team
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Scope, plan, estimate and delivers projects, according to aligned roadmaps. Proactively providing projects updates, identifies impediments, project risks and options for mitigation
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Collaborate with data analysts, data engineers and other stakeholders to deliver data solutions that drive insights and support business needs
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Automate repetitive data tasks (testing, deployment, etc.), implement monitoring solutions, and support the production environment to ensure smooth data operations
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Mentor and provide guidance to junior engineers and contribute to the continuous improvement of engineering practices across the team
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Implement data governance practices, ensuring data security, privacy, and compliance with industry standards and regulations (e.g., GDPR)
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A degree in a MINT field or an equivalent educational background
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At least 3-5 years of experience in data engineering, including working with large-scale data processing and management systems
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Demonstrated practice in Python, SQL, Pyspark and DevOps implementation (Azure Devops, Jenkins)
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Experience in design and implementation of complex data pipelines
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Extensive experience in clean, maintainable, and efficient code development
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Strong communication skills in English and the ability to work effectively with both technical and non-technical stakeholders