Data Infrastructure Manager

Data Infrastructure Manager - Microsoft AI

Microsoft AI is looking for passionate leaders to tackle challenging AI questions and build systems with true artificial intelligence. Our vision is to make AI accessible to everyone.

About the Role

We're seeking a Data Infrastructure Manager to lead a team of engineers building and scaling data infrastructure for Microsoft's consumer AI. This role involves technical leadership and people management, setting technical direction for large-scale data and ML pipelines, AI agentic workflows, and intelligent systems. You'll multiply your impact through others while working on exciting AI infrastructure challenges.

You'll Bring

• Deep technical expertise in big data and distributed systems.
• A track record of leading and developing engineering talent.
• A passion for automation, observability, and operational excellence.
• The ability to translate complex technical strategy into clear, executable plans.
• Empathy, collaboration, and a growth mindset.

Microsoft's Mission and Values

Microsoft's mission is to empower every person and every organization to achieve more. We foster a growth mindset, innovate to empower others, and collaborate to achieve shared goals, building on values of Respect, Integrity, and Accountability in an inclusive culture.

Work Arrangement Policy

Starting January 26, 2026, Microsoft AI (MAI) employees within a 50-mile commute of a designated U.S. office or 25-mile commute of a non-U.S. office are expected to work from the office at least four days per week. This is subject to local law and jurisdiction.

Responsibilities

Team Leadership & People Development

• Hire, mentor, and develop Data Infrastructure Engineers, fostering technical excellence and growth.
• Conduct 1:1s, set clear goals, and provide feedback for career development.
• Build and sustain an inclusive, collaborative team environment aligned with Microsoft's values.

Technical Strategy & Architecture

• Define and drive the technical vision for scalable, reliable Big Data Infrastructure for AI applications.
• Lead technical design reviews, establish engineering standards, and ensure code quality.
• Architect data solutions across storage, compute, and analytics, including pipelines for AI agent workflows.

Platform & Operations

• Champion DevOps and SRE best practices, including automated deployments, monitoring, and incident response.
• Guide the team in building a self-service big data platform.
• Oversee CI/CD pipelines and infrastructure-as-code using tools like Bicep, Terraform, and ARM.
• Lead capacity planning and proactively resolve bottlenecks.

Cross-Functional Collaboration

• Partner with Data Engineers, Data Scientists, AI Researchers, and Developers on big data workflows.
• Collaborate with Security teams on infrastructure security practices.
• Represent the team in planning and prioritization discussions.

Qualifications

Required Qualifications

• Bachelor's Degree in a related field AND 6+ years of relevant experience, OR Master's Degree AND 4+ years of relevant experience, OR equivalent experience.

Preferred Qualifications

• Master's Degree and 10+ years of technical experience, OR Bachelor's Degree and 14+ years, OR equivalent experience.
• 5+ years in Big Data Infrastructure, DevOps, SRE, or Platform Engineering.
• 5+ years of hands-on experience with distributed systems (bare-metal to cloud-native).
• 5+ years overseeing containerized application deployments (Kubernetes, Helm/Kustomize).
• Solid scripting and automation fluency (Python, Bash, PowerShell).
• Proven track record managing CI/CD, release automation, and incident response.
• Hands-on expertise with modern data platforms (e.g., Databricks, SQL/NoSQL databases, Spark, HDFS, ADLS Gen2, Event Hub, Kafka).
• Proven experience with cloud-native infrastructure (Azure, AWS, GCP).
• Strong collaboration experience with various technical teams.
• Experience with agentic workflow infrastructure (orchestration, retrieval pipelines).
• Familiarity with modern web stacks (TypeScript, Node.js, React, PHP).

Key skills/competency

• Data Infrastructure Management
• Big Data Platforms
• Distributed Systems
• Machine Learning Pipelines
• AI Agentic Workflows
• DevOps & SRE
• Cloud Infrastructure (Azure, AWS, GCP)
• Technical Leadership
• People Management
• Data Architecture