Principle Data Engineer

Microsoft•,

12h•$142,800 - $304,200•Remote

About The Position

MCAPS-Core accelerates customer outcomes and business growth. By uniting product, engineering, marketing, sales, customer success, and partners around a common customer mission, we help customers realize value faster and turn innovation into measurable business impact. Through deep technical expertise and differentiated go-to-market execution, we win competitively, capture market share, and scale repeatable growth motions. We differentiate through value creation - delivering world-class, AI-powered customer experiences that accelerate adoption, strengthen loyalty, and create sustainable competitive advantage, driving growth that outpaces the market. MCAPS-Core Data & Applied AI (DAAI) is a centralized data and AI organization driving Microsoft's transformation through enterprise data platforms, advanced analytics, AI-powered solutions, automation, and governance. Our mission is to turn data into intelligence and intelligence into action, empowering teams across Microsoft to accelerate decision-making, increase productivity, and deliver exceptional customer outcomes through innovative customer-zero solutions. MCAPS-Core DAAI is seeking a Principal Data Engineer to build the AI-powered platforms, intelligent agents, and data foundations that fuel Microsoft's transformation. This role combines deep software engineering, data engineering, and AI expertise to deliver scalable solutions powered by generative AI, LLMs, agentic workflows, and enterprise data platforms. The ideal candidate is a hands-on technical leader who thrives on solving complex engineering challenges and building intelligent systems that translate data into actionable business impact. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

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.
5+ years of demonstrated experience working in data-intensive Azure environments and translating business requirements into scalable data and AI solutions.
Deep expertise with Microsoft technologies, including Microsoft Fabric, Azure AI Foundry, Azure Data Platform, Synapse Analytics, Azure App Service.
Hands-on experience developing solutions using Python and/or other modern programming languages such as C#, Java, Scala, or equivalent.
Proven experience designing and implementing Generative AI, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), semantic search, vector database, and agentic AI solutions.
Ability to meet Microsoft, customer and / or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire / transfer and every two years thereafter.

Nice To Haves

Master's Degree in Computer Science, Math, Software Engineering, Computer Engineering, or related field AND 8+ 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 12+ years experience in business analytics, data science, software development, data modeling, or data engineering OR equivalent experience.
Experience building and deploying Copilots, intelligent agents, multi-agent systems, and enterprise AI applications.
Expertise in AI platform engineering, LLM orchestration, knowledge graphs, prompt engineering, model tuning, and AI evaluation frameworks.
Expertise in developing and operating cloud-scale services using modern software engineering and SRE practices, with a focus on reliability, observability, automation, and service excellence.
Experience with MLOps, LLMOps, AI observability, model governance, and production AI operations.
Proven success leading enterprise-scale cloud, data, analytics, and AI transformation initiatives.
Demonstrated ability to mentor engineers, foster innovation, and elevate engineering excellence across large, complex organizations.
Strong technical leadership skills with a track record of driving architecture decisions, engineering strategy, and cross-functional execution.

Responsibilities

Lead end-to-end development of cloud-native applications, APIs, and platform services, applying full-stack engineering principles across frontend, backend, integration, security, observability, testing, and continuous delivery disciplines.
Architect and deliver next-generation AI platforms, copilots, agentic systems, and intelligent applications powered by Generative AI, Large Language Models (LLMs), and advanced reasoning capabilities.
Design and build AI-ready data platforms and knowledge systems, scalable AI services, APIs, and orchestration frameworks that power autonomous agents, intelligent automation, and enterprise-scale workflows.
Drive the technical strategy and architecture for Applied AI solutions, partnering across engineering, data science, product, and business teams to accelerate AI innovation and adoption.
Create reusable AI frameworks, SDKs, and platform services that improve developer productivity, model performance, scalability, governance, and reliability.
Establish best practices for Responsible AI, AI governance, model evaluation, security, compliance, observability, and operational excellence.
Provide hands-on technical leadership through architecture reviews, code reviews, mentoring, and direct contribution to software, data, and AI solutions.
Partner with business and technology leaders to identify and deliver transformational AI opportunities that enhance productivity, decision-making, and customer experiences.
Champion customer-zero innovation by turning emerging AI technologies into scalable platforms, reusable services, and enterprise-wide solutions.