Principal Software Engineering Manager - Data Analytics

• Lead the design and delivery of world-class experiences for a new big data cloud offering, with emphasis on scale, reliability, and performance.
• Manage and grow a team building core infrastructure services for data engineering and analytics workloads (compute, runtime services, job/session management, configuration, platform integrations).
• Own technical strategy and execution end-to-end: translate product requirements into architecture, milestones, and high-quality production outcomes.
• Drive operational excellence by establishing troubleshooting practices (logs, metrics, traces), guiding root-cause analysis, and converting operational learnings into engineering improvements.
• Improve platform scalability, resiliency, and observability, including automation to reduce operational toil
• ensure best practices are adopted consistently across the team.
• Partner cross-functionally with product and engineering leaders to deliver end-to-end features, align priorities, and continuously raise the quality bar.
• Coach and mentor engineers, provide technical guidance and performance feedback, and foster a culture of ownership, high standards, and continuous learning.

• 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.
• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
• This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
• 4+ years people management experience.
• Software engineering foundation (data structures, algorithms, testing, debugging, performance) with the ability to guide and review technical decisions.
• Demonstrated experience leading teams that build and ship production infrastructure (backend services, distributed systems, platform components) in cloud environments.
• Understanding of distributed systems concepts, including fault tolerance, scaling, scheduling, and resource management, and ability to apply them to team-level architecture.
• Proficiency in at least one backend/system language (e.g., Java, Scala, C#, C++, Python) and the ability to stay hands-on enough to unblock teams and assess designs.
• Proven ability to ramp up quickly in new domains, tools, and codebases
• growth mindset and learning agility.
• Ability to operate effectively in an AI-powered engineering environment—adopting AI-assisted workflows (copilots/agents), improving team productivity, and elevating quality.
• Experience leading large-scale infrastructure efforts for data platforms or compute services (e.g., orchestration, runtime services, cluster/resource management, multi-tenant systems).
• Experience establishing and running observability and operations programs (SLOs/SLIs, alerting, incident response, postmortems) and improving reliability over time.
• Background in performance and reliability engineering (profiling, optimization, capacity planning, cost/performance tradeoffs).
• Familiarity with cloud-native operating models: service ownership, CI/CD, safe deployments, automation, and modern incident management practices.

Experience with Spark and/or big data systems is a big plus (but not required if you’re eager to learn).