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A Snapshot of your DayPassionate about the environment and climate change? Ready to be part of the future of the energy transition? The Siemens Energy Enterprise Data & Advanced Analytics organization creates and enables Data and AI-driven solutions and the corresponding governance and platform to grow and optimize the Siemens Energy businesses and operations. Given the scale of the challenge we need many types of perspectives to help reimagine the future. And honestly, we can't do it alone. Our team is looking for innovative, enthusiastic, and versatile data, digital, and AI professionals that will drive us forward on this exciting venture.The Data Strategy & Innovation tower has the responsibility of developing and delivering the enterprise data and AI strategy to align with business priorities and outcomes, while pushing R&D efforts to advance leading edge analytics capabilities. We are also responsible for managing portfolio of investments, external relations, partnerships, and allocation of funds to meet our organization's objectives.As the principal data engineer for our Enterprise Data & Advanced Analytics organization, you have the opportunity to reengineer how we provision and make data safe, reliable, and accessible to all of Siemens Energy.How You'll Make An Impact / ResponsibilitiesDrive technical delivery and be accountable for overall data engineering strategy, data orchestration architecture, and our data operations execution framework.Define the target data architecture for Siemens Energy's data estate, considering the construct of the business areas, data domains, data products and the anticipated integration and provisioning for multi-purpose consumption patterns.Act as a product owner of our data platform and technology services to define the platform requirements and roadmap, and contribute to the design of the underlying infrastructure to facilitate data sharing, data provisioning with the necessary access management control to achieve federated data governance, i.e. data mesh framework.Define the target operating model of a modern data and AI factory, and create the framework to monitor and manage the performance and quality of integration pipelines, and to measure and improve our data development velocity and efficiency.Define standards and design patterns to optimize data sharing without sacrificing flexibility, promote scale and a composable data landscape that will serve our current and future data needs.Provide good foundational frame for cost management and control for our data and platform operations.Collaborate with data owners and data delivery teams to design and execute key initiatives, defining minimum viable product (MVP) scope for the upcoming initiatives and outline future roadmap of enhancements and potential improvements.
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