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 delivers projects, according to aligned roadmaps. Proactively providing projects updates, identifies 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 3-5 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: