Senior Data Engineer
3 semanas atrás
As a Senior Data Engineer at Siemens Energy, you will play a key role in improving the availability and quality of existing data and creating a high-quality architecture for the sustainable implementation of AI use cases. You will work closely with data engineers, data scientists, central functions, and external partners to code, consult, and provide advice during this process. Your role will also involve business trips to understand and support local processes.
Key Responsibilities- Work in an interdisciplinary team with data engineers, scientists, and business professionals to foster a data-driven company culture approach and implement machine learning and AI use cases.
- Support and consult business professionals in use case refinements to identify promising use cases with high business impact.
- Collection, maintenance, preparation, enrichment, validation, and distribution of data.
- Mentor staff in data acquisition techniques and ensure proper data collection/storage for administrative processes and on-site.
- Plan and create a suitable system infrastructure for applications for analysis.
- Design and configure robust data sets and databases.
- Maintain and develop existing data infrastructure.
- Support conception and provision of a system architecture.
- Build up deep knowledge with Snowflake model.
- University degree in computer or data science or related field.
- Proven track record and/or several years of experience in data acquisition techniques.
- Deep knowledge of Snowflake SQL Scripting, Alteryx, SnapLogic.
- Experience in DevOps and familiarity with GitLab for CI/CD.
- Deep knowledge in building end-to-end data pipelines.
- Experience in data migration.
- Project management skills and agile approach.
- Highly motivated, proactive, and self-directed.
- Excellent interpersonal and intercultural communication skills.
- Willingness to travel with a focus on central Europe (~30%).
- Very good English skills.
Basic research is a key driver for innovation within Grid Technologies. We drive our own innovation campaigns, collaborate with in- and external partners, and directly support product development with profound technological expertise.
During this process, we create and collect a huge amount of data which we need to take a closer look at. In the upcoming years, we will further deep dive into the potentials of process digitalization, machine learning, and generative AI.
Therefore, we need high-quality, validated data available in the right format and structure, to further evaluate them.
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