Job Description

• Design and implement scalable, reliable, and efficient data architecture to support large-scale data processing and analytics needs.
• 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.
• Scope, plan, estimate and deliver projects according to aligned roadmaps. Proactively provide project updates, identify impediments, project risks and options for mitigation.
• Collaborate with data analysts, data engineers and other stakeholders to deliver data solutions that drive insights and support business needs.
• Automate repetitive data tasks (testing, deployment, etc.), implement monitoring solutions, and support the production environment to ensure smooth data operations.
• Mentor and provide guidance to junior engineers and contribute to the continuous improvement of engineering practices across the team.
• Implement data governance practices, ensuring data security, privacy, and compliance with industry standards and regulations (e.g., GDPR).

• A degree in a MINT field or an equivalent educational background.
• At least 3 years of experience in data engineering, including working with large-scale data processing and management systems.
• Demonstrated practice in Python, SQL, Pyspark and DevOps implementation. (Azure Devops, Jenkins)
• Experience in design and implementation of complex data pipelines.
• Extensive experience in clean, maintainable, and efficient code development.
• Strong communication skills in English and the ability to work effectively with both technical and non-technical stakeholders.

Your ZEISS Recruiting Team:

Sturcz Noémi, Wenner Lili