Microsoft Product Manager II 

Taiwan, Taoyuan City 

800100140

As ain our Power Data Engineering team, you will drive the transformation of our data engineering pipelines and spearhead telemetry-based anomaly detection efforts that ensure Windows does not regress on battery life or performance. This opportunity is a strategic role at the intersection of data engineering and system fundamentals. You will define and execute plans to improve efficiency, automation, and reliability of data pipelines, partner with cross-functional teams to implement machine learning telemetry systems, and deliver deep insights that accelerate improvements in Windows power, performance, sustainability, and more.

Required Qualifications:

• Bachelor's Degree AND 2+ years experience in engineering, product/technical program management, data analysis, or product development
  • OR equivalent experience.
• 1+ year(s) of experience managing cross-functional and/or cross-team projects.
• Proven track record of driving process improvements or tooling initiatives in an engineering or data-focused domain.
• Excellent problem-solving and analytical skills, with a data-driven mindset.

Preferred Qualifications:

• Solid communication skills: Excellent written and verbal communication in English, with ability to convey technical concepts to diverse audiences.
• Analytical & telemetry experience: Understanding of telemetry, monitoring, or diagnostics tools to derive insights (e.g. performance profilers, dashboards, Azure Monitor).
• Cross-functional influence: Demonstrated ability to lead cross-functional initiatives and influence stakeholders without direct authority.
• Data engineering knowledge: Familiarity with modern data pipeline technologies and automation frameworks – e.g. orchestration tools like Azure Data Factory, big data processing, CI/CD pipelines.
• Fundamentals domain expertise: Understanding of system fundamentals in reliability, performance, or power domains; experience with telemetry-based anomaly detection or machine learning in an engineering context is a strong plus.
• Operational excellence: Passion for building inclusive, customer-focused products and driving continuous improvements in product quality and fundamentals.

• You will partner with Engineering, Data Platform, and Feature teams to align requirements and execution for fundamentals initiatives. Facilitate knowledge sharing and coordinate efforts across reliability, performance, and power teams to achieve common goals.

• You will drive the end-to-end optimization of data pipelines and workflows for platform health metrics. Identify process bottlenecks and implement lean, automated solutions (e.g. CI/CD, Infrastructure as Code) to streamline data ingestion and processing. Introduce modern orchestration tools (e.g. Azure Data Factory, Synapse) to improve efficiency and throughput.
• You will partner with Engineering to enhance the reliability and robustness of our data infrastructure. Champion fault-tolerant pipeline architectures with automated error handling and recovery mechanisms. Establish real-time monitoring, alerting, and comprehensive automated testing to ensure high availability and data quality.
• You will work with Engineering to develop and refine telemetry pipelines and anomaly detection systems to proactively identify power or performance regressions in Windows. Leverage large-scale telemetry data (e.g. battery drain, CPU usage signals) and machine learning to detect distribution shifts and trigger early corrective actions. Transform raw data into actionable insights and auto-filed bugs that help engineering teams resolve issues before they reach customers.
• You will clearly articulate goals, findings, and trade-offs to both technical and non-technical stakeholders. Prepare executive-ready updates on pipeline health, anomaly detection outcomes, and data quality improvement plans. Influence partner teams by making data-driven cases for investments in fundamentals and driving consensus on priorities.
• You will foster a culture of continuous learning and operational excellence. Use data and experimentation to measure impact, iterate on processes, and elevate our fundamentals strategy over time. Track key performance indicators (e.g. pipeline uptime, mean time to recovery, anomaly detection precision) and drive ongoing refinements to exceed targets.