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Principal Engineer Manager Azure Storage
Microsoft
Original Job Summary
About the Role
The Principal Engineer Manager Azure Storage at Microsoft is poised to shape the future of AI-scale storage solutions. You will lead a high-impact team focused on enhancing the storage control plane and disks platform to meet the demands of rapidly growing AI-scale workloads. This role involves driving AI-powered innovations, resolving system bottlenecks, optimizing performance and resiliency, and collaborating across organizational boundaries for platform-wide improvements.
Responsibilities
- Collaborate with stakeholders to determine user requirements.
- Lead teams to develop design documents and monitor dependencies.
- Optimize, debug, refactor and reuse code for performance improvements.
- Coordinate project plans and release plans with multiple groups.
- Oversee product development and scaling to meet customer requirements.
Qualifications
Required: Bachelor's Degree in Computer Science or related discipline with 6+ years technical engineering experience including coding in C# or equivalent, hands-on cloud and distributed systems experience, and 2+ years as a technical lead.
Preferred: Advanced degree or additional years of experience, 4+ years people management experience, security screening compliance.
Compensation & Application
This role includes a competitive pay range (USD $139,900 - $274,800, with higher ranges in select locations) and benefits. Microsoft accepts applications until October 15th, 2025. Additional benefits and pay information are available at Microsoft Careers.
Key skills/competency
- Distributed Systems
- Azure Storage
- Cloud Architecture
- Performance Optimization
- Scalability
- AI-scale Workloads
- C#
- Leadership
- Software Development
- Team Collaboration
How to Get Hired at Microsoft
🎯 Tips for Getting Hired
- Research Microsoft culture: Study mission, values, and team insights on LinkedIn.
- Customize your resume: Tailor technical and leadership experiences.
- Highlight distributed systems: Emphasize cloud and AI workload expertise.
- Prepare with examples: Demonstrate successful large-scale projects.