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.

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You will play a crucial role in shaping the future of the company's Operational Metadata, Enterprise Data Management framework and actively contribute to the success of the digital transformation at ZEISS. In this challenging leadership role, you will lead a team that focuses on identifying, structuring and operationalizing metadata across ZEISS. You will shape these ideas from inception to launch working closely together in joint, co-located projects with the ZEISS business units as well as our Design & Insights and Architecture & Engineering functions at ZEISS Digital Partners. You will define the cornerstones of scalable data and analytics solutions to provide a superior customer experience and ensure their organizational implementation together with our business units. With your team and your fellow colleagues, you will build our capability to stay on top of digital innovation and make sure that decisions regarding the use of data are made in the interest of ZEISS as well as our customers. Your direct roles and responsibilities are:

• You will build highly scalable data products for the automated consolidation, processing and analysis of large amounts of data for machine learning

• from conception to prototyping to operation, batch and streaming

• You will evaluate the latest trends and technologies, help shape our architecture decisions including the DevOps / DataOps processes and drive innovative solutions for data management in the machine learning context

• You contribute to ensuring continuous quality, reusability and performance improvement and take responsibility for applications and stakeholders during development, commissioning and operation

• Automating and deploying data transformations and pipelines on the provided Data Platform

• Working in an accurate (clean-code) and test-driven DEVOPS Software Development

• In close cooperation with data scientists, you implement exciting AI use cases (e.g. entity extraction, recommender, chatbots) with data of all kinds (e.g. time series, images, documents)

• An excellent university degree in computer science, business informatics, mathematics or in a related field

• According to experience, 3-7 years of relevant professional experience in IT

• Professional development skills in Python/Java/Scala

• Experience with Cloud technologies like Microsoft Azure or similar environment

• Distributed processing frameworks: Spark-based data transformations (Databricks, Data Factory), search engines (Elastic), graph databases and analytical or in-memory database systems (SQL and No-SQL)

• Productionalising and publishing data services as scalable, performant Data APIs

• Strong Experience in Data Modelling, Pipeline Orchestration and Scheduling (ex: Airflow) and Data Artifacts deployment (ex: Azure DEVOPS, Gitlab)

• Professional Experience in multiple forms of Data Storages (Azure Blob, Synapsis) and their interrogation (SQL)

• Extent development of serverless (Azure Data Factory, Synapse Analytics) or container based applications (Kubernetes)

• Monitoring and logging (Elastic, Datadog)

• Technical development and integration of User-Request Workflows through reusable Rest APIs

• Experience in machine learning and deep learning algorithms productionalisation in Cloud Best-practice and implementation for continuous integration / delivery Cloud-based development artifacts (Terraform)

• Agile Software-development using Scrum / Kanban

• An independent, conscientious and solution-oriented way of working

• Fluency in English