Job Description
Microsoft’s Cloud business is expanding, and the Cloud Supply Chain (CSCP) organization is responsible for enabling the infrastructure underlying this growth including AI! CSCP’s vision is to empower customers to achieve more by delivering Cloud Capacity Differentiated at Scale. Our mission is to deliver capacity for all cloud services predictably through intelligent systems driven by continuous learning and a world class organization. The CSCP Organization is responsible for traditional supply chain functions such as Plan, Source, Make, Deliver, but also manages supportability (spares), decommissioning and disposition of Data center assets worldwide. We deliver the core infrastructure and foundational technologies for Microsoft's over 200 online businesses including Bing, MSN, Office 365, Xbox Live, Skype, OneDrive and the Microsoft Azure platform for external customers. Our infrastructure is comprised of a large global portfolio of more than 200 datacenters supporting services for more than 1 billion customers in over 90 countries worldwide.As part of the Decision Sciences Engineering team in Azure, we are at the heart of Microsoft’s cloud data strategy. Our mission is to deliver the data for our customers & partners by combining the best of Data science and Engineering culture of delivering platforms and services on Azure Data platforms. Our Culture : Growth mindset – we practice a learn it all vs a know-it-all attitude. Wins are cheered, failures are celebrated and viewed as learning opportunities. Diverse and Inclusive – every voice matters and is appreciated, where you can express yourself. Leadership oriented – each of us are leaders who strive to create clarity, generate energy, and deliver success. We are looking for a Data Engineer, who can demonstrate the following traits: • A team player who is self-starter, customer obsessed, highly inquisitive and biased towards action. • Comfortable with cold start problems, thrive in ambiguity with an eye for detail. • Delivers results through innovation and persistence. • Demonstrate depth in areas of Data Engineering, especially depth in Spark, • Demonstrates a product mindset and looks to deliver re-usable components that can be deployed as-is or into existing pipelines. • Have a flair for Artificial Intelligence/Machine Learning technology.
