Principle Data Engineer

Microsoft is a technology company whose MCAPS-Core Data & Applied AI organization drives transformation through enterprise data platforms, advanced analytics, AI-powered solutions, automation, and governance. The Principal Data Engineer will build scalable AI-powered platforms, intelligent agents, data foundations, and cloud-native services using generative AI, LLMs, agentic workflows, and enterprise data platforms. The role provides hands-on technical leadership across architecture, engineering strategy, responsible AI, governance, and enterprise-scale AI delivery.

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

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
• • 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