Principal Data Engineer (CoreAI)

Microsoft•Redmond, WA

About The Position

Your responsibilities will include designing and maintaining data pipelines, curating high-quality datasets, performing in-depth data analysis, developing dashboards, and building the data and AI infrastructure that supports these capabilities and our missions. Shape the Product Vision: Define and influence the product roadmap by aligning technical strategy with business goals and customer needs. Architect for Scale and Sustainability: Design and evolve durable, scalable system architectures that balance long-term maintainability with short-term delivery needs, making thoughtful engineering trade-offs. Direct Impact: Leads by example and mentor others to produce extensible and maintainable code used across the platform. Creates and applies metrics to drive the quality and stability of code, as well as appropriate coding patterns and best practices. Foster Engineering Alignment: Build consensus across engineering teams and partner organizations by driving clarity, alignment, and shared ownership of technical direction. Build Foundational Capabilities: Contribute to and lead the development of core platform components and reusable building blocks that accelerate team velocity and product innovation. Mentor and Grow Talent: Support the growth of engineers through mentorship, knowledge sharing, and by enabling long-term ownership of key systems. Champion Customer-Centric Development: Engage directly with customers and product teams to capture feedback, understand demand signals, and refine product messaging—ensuring the voice of the customer shapes product evolution. Operational Excellence: Drive operational excellence in managing and operating large-scale data systems with a high bar for data quality, stability, security and privacy. Set high standards for reliability and performance.

Requirements

Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 4+ years experience in business analytics, data science, software development, data modeling, or data engineering OR Bachelor's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 6+ years experience in business analytics, data science, software development, data modeling, or data engineering OR equivalent experience.
These requirements include but are not limited to the following specialized security screenings:
Demonstrated expertise in building, evolving and leading complex data platforms.
Experience with data visualization technologies such as PowerBI, Tableau, Grafana, or similar.
4+ years of experience working with complex data sets, data pipelines and solving challenging data problems at scale.

Responsibilities

Designing and maintaining data pipelines
Curating high-quality datasets
Performing in-depth data analysis
Developing dashboards
Building the data and AI infrastructure
Shape the Product Vision: Define and influence the product roadmap by aligning technical strategy with business goals and customer needs.
Architect for Scale and Sustainability: Design and evolve durable, scalable system architectures that balance long-term maintainability with short-term delivery needs, making thoughtful engineering trade-offs.
Direct Impact: Leads by example and mentor others to produce extensible and maintainable code used across the platform. Creates and applies metrics to drive the quality and stability of code, as well as appropriate coding patterns and best practices.
Foster Engineering Alignment: Build consensus across engineering teams and partner organizations by driving clarity, alignment, and shared ownership of technical direction.
Build Foundational Capabilities: Contribute to and lead the development of core platform components and reusable building blocks that accelerate team velocity and product innovation.
Mentor and Grow Talent: Support the growth of engineers through mentorship, knowledge sharing, and by enabling long-term ownership of key systems.
Champion Customer-Centric Development: Engage directly with customers and product teams to capture feedback, understand demand signals, and refine product messaging—ensuring the voice of the customer shapes product evolution.
Operational Excellence: Drive operational excellence in managing and operating large-scale data systems with a high bar for data quality, stability, security and privacy. Set high standards for reliability and performance.