Define and drive the long-term technical vision and architecture for the Fabric Data Warehouse Distributed Query Processing (DQP) platform, ensuring scalability, reliability, performance, and operational excellence at cloud scale. Lead the design and evolution of core query processing technologies, including distributed execution, scheduling, resource management, workload isolation, and performance optimization. Identify and solve fundamental distributed systems problems involving scale, concurrency, fault tolerance, elasticity, efficiency, and service reliability. Establish architectural standards, engineering best practices, and technical governance processes through design reviews, technical mentoring, and cross-team collaboration. Influence the long-term platform strategy for personalized and collaborative analytics experiences, balancing customer value, scalability, security, and maintainability. Mentor and develop senior engineers and technical leaders, fostering a culture of innovation, technical excellence, and customer obsession. Champion the use of AI-driven development practices, automation, and intelligent observability to improve engineering productivity, accelerate problem resolution, and enhance the health, reliability, and operational efficiency of the platform. Embody our culture and values. ​​Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.​ Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Strong distributed systems experience and understanding of scale, resiliency, and reliability patterns. Strong AI-first mindset and growth orientation, with a passion for exploring new technologies and ways of working to improve engineering effectiveness and customer outcomes.