Principal Software Engineer

Microsoft•Redmond, WA

20h•$165,600 - $331,200

About The Position

The Microsoft Dataverse Intelligence Platform organization is building an AI-native business data and intelligence platform that connects business data, semantic understanding, governance, analytics, process intelligence, and AI-powered execution at cloud scale. The platform supports Microsoft Dynamics 365, Power Platform, Copilot experiences, and customer solutions worldwide. The Principal Software Engineer will provide technical leadership across complex platform systems, helping define strategy, drive architecture, modernize services, advance AI-native engineering practices, and influence technical direction across multiple product areas. The work may span AI-native business applications and agents, business data platforms and semantic intelligence, process intelligence and automation, enterprise knowledge and retrieval systems, distributed systems and cloud infrastructure, or AI-native developer experiences. The role partners with engineering, product, research, data science, security, and business teams to address ambiguous technical challenges, build durable platform capabilities, and deliver customer impact at global scale. Microsoft describes its culture as grounded in a growth mindset, innovation, collaboration, respect, integrity, accountability, and inclusion.

Requirements

Bachelor's degree in Computer Science or a related technical field and 8+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.
Strong programming experience in C#, Java, Python, JavaScript, C++, or similar modern development languages.
Ability to create clarity in ambiguous environments, influence technical direction without formal authority, and align stakeholders around long-term platform investments and architectural decisions.
Solid communication skills, including the ability to translate complex technical concepts into clear engineering, product, customer, and business outcomes.
Demonstrated experience mentoring senior engineers, providing cross-organization technical leadership, and raising the engineering bar across large engineering teams.

Nice To Haves

Master's degree in Computer Science or a related technical field and 12+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python; or a bachelor's degree in the same fields and 15+ years of such experience; or equivalent experience.
Experience designing, building, and operating large-scale distributed systems, cloud platforms, data platforms, AI services, or enterprise software systems.
Experience building foundational platform capabilities serving multiple products, services, customers, or engineering organizations at scale.
Deep expertise in software architecture, distributed systems, data architecture, reliability engineering, scalability, performance optimization, and operational excellence.
Experience with Azure, cloud-native architectures, containerized workloads, platform services, and hyperscale cloud infrastructure.
Experience with business data platforms, knowledge systems, enterprise data services, semantic models, metadata platforms, retrieval systems, search technologies, or intelligence platforms.
Experience developing AI-powered applications, LLM-enabled experiences, retrieval and grounding systems, agent frameworks, intelligent automation, production AI services, or AI-native developer experiences.
Experience applying AI throughout the software development lifecycle, including AI-assisted engineering, code generation, testing, quality validation, deployment automation, and operational intelligence.
Experience with CI/CD, infrastructure as code, automated testing, observability, telemetry, incident management, and DevOps.
Understanding of security, privacy, governance, compliance, reliability, accessibility, and Responsible AI principles for enterprise-scale AI systems.

Responsibilities

Lead architecture, technical strategy, and design for large-scale business data, intelligence, AI, and cloud platform systems; define technical direction across foundational capabilities, intelligent services, and cross-organization initiatives.
Design and evolve cloud-native distributed systems for enterprise business applications, data platforms, copilots, agents, and AI-driven experiences, with attention to scalability, reliability, performance, security, governance, compliance, and operational excellence.
Lead development of AI-native platform capabilities, including enterprise retrieval and grounding, semantic intelligence, process intelligence, agent orchestration, intelligent automation, and LLM-powered experiences.
Drive the evolution of Dataverse as a business data and intelligence platform for the AI era.
Promote AI-native engineering practices throughout the software development lifecycle to accelerate innovation, improve developer productivity, and raise engineering quality.
Define platform architecture and modernization strategies that improve consistency, extensibility, resiliency, developer experience, and maintainability across Microsoft's business application ecosystem.
Partner with engineering leaders, product managers, architects, researchers, data scientists, and business stakeholders to shape roadmaps, influence investments, and deliver valuable platform capabilities.
Evaluate architectural tradeoffs across data platforms, intelligence layers, AI systems, and distributed services, balancing customer needs, scalability, governance, cost efficiency, and long-term sustainability.
Establish reusable platform patterns, frameworks, APIs, data models, and intelligence services for teams across Microsoft.
Champion engineering excellence through secure development, automation, CI/CD, observability, testing, telemetry, incident management, and operational rigor.
Mentor and develop engineers through technical leadership, architecture reviews, design coaching, AI-native engineering practices, and leadership by example.
Foster customer obsession, innovation, accountability, continuous learning, and inclusive collaboration while helping teams navigate ambiguity and deliver impact at scale.