Overview Microsoft Industry Solutions - Global Center Innovation and Delivery Center (GCID) delivers end-to-end solutions by enabling accelerated adoption and productive use of Microsoft technologies. An organization of well over 1000+ exceptional people, GCID presents a great opportunity for highly skilled services professionals to make a foray into consulting, solution development and delivery roles. The Principal Consultant is a senior leader responsible for the successful technical execution and delivery of complex client projects across diverse domains. This role acts as a strategic anchor between clients, architects, delivery managers, project managers, and delivery teams. In the AI-first GCID organization, Principal Consultants are expected to embed AI-native thinking into delivery models, ensuring solutions are intelligent, scalable, and aligned with business outcomes. The ideal candidate is passionate about technology, demonstrates breadth of expertise, and advocates for solutions that deliver true client value. Responsibilities AI-First Delivery Leadership Embed AI-first principles into delivery workflows, leveraging automation and intelligent orchestration where applicable. Lead end-to-end delivery of complex projects, ensuring solutions are scalable, robust, and aligned with client business outcomes. Drive engineering excellence through reusable components, accelerators, and scalable architecture. Oversee technical execution across multiple projects, ensuring adherence to best practices, quality standards, and compliance requirements. Collaborate with clients and internal stakeholders to define strategies, delivery plans, milestones, and risk mitigation approaches. Act as a technical point of contact for clients, translating business requirements into scalable technical solutions. Ensure delivery models are optimized for modern AI-native execution, including integration of automation and intelligent processes. Ability to step into at‑risk projects, quickly assess issues, and establish a credible path to recovery or exit. Engineering Excellence Champion high-quality engineering practices across all delivery engagements. Ensure adherence to coding standards, architectural integrity, and performance benchmarks. Define and institutionalize engineering guardrails that embed secure coding, test‑driven development, observability, and performance best practices by default. Encourage continuous learning and technical certifications to maintain cutting-edge expertise. Drive adoption of modern delivery methodologies (Agile, DevOps, CI/CD) to ensure robust and scalable solutions. Foster a culture of technical rigor, innovation, and accountability within the team. Innovation & Thought Leadership Use design thinking to shape user‑centric solutions, aligning business goals, architecture decisions, and delivery execution. Monitor and evaluate emerging technologies to inform strategic direction. Lead innovation in delivery models, reusable assets, and accelerators to enhance efficiency and impact. Champion modern thinking and best practices across teams and engagements to foster a culture of continuous improvement. Client Engagement & Solutioning Engage with clients to understand business needs and provide expert guidance throughout the project lifecycle. Support pre-sales and solutioning efforts with estimations, proof-of-concepts, and technical proposals. Build and maintain strong client relationships, ensuring high levels of satisfaction and value delivery. Partner with client leadership to drive the cultural shift required for "AI-native" operations, moving beyond technical implementation to user adoption and workflow transformation. Team Management & Mentorship Lead and mentor cross-functional teams, fostering a culture of learning, collaboration, and technical excellence. Conduct reviews, provide feedback, and support professional development of team members. Quality & Compliance Ensure secure, compliant, and reliable solution delivery through secure coding, test driven development, observability, design reviews, and quality gates across all engagements. Strategic Partnering Serve as a strategic partner for internal and external stakeholders on key initiatives. Provide strategic guidance and execution oversight to ensure alignment with organizational goals. Define and track specific Business KPIs (e.g., revenue uplift, operational cost reduction, customer CSAT improvement) associated with AI initiatives. Qualifications 20+ years of experience in software/solution engineering, with at least 3–5 years in delivery leadership roles. Proven experience in leading delivery of complex, multi-disciplinary projects. Strong understanding of modern delivery methodologies (Agile, Scrum, DevOps, etc.). Excellent communication, stakeholder management, problem-solving, and team leadership skills. Bachelor’s degree in computer science, Engineering, or related field (or equivalent experience). Relevant certifications are a plus. Areas of Expertise Enterprise Data Architecture & Modern Platforms Lead enterprise data modernization initiatives in close collaboration with Enterprise and Solution Architects, spanning architecture assessment, target‑state design, hands‑on implementation, and optimization. Co‑define architectures with Architects where metadata, lineage, classification, and data discovery are first‑class capabilities, enabling governed, trusted analytics and AI consumption at scale. Drive and influence architectural decisions for modern data platforms, providing hands‑on delivery leadership across Microsoft Fabric (OneLake, Warehouse, Lakehouse, Event streams, Real‑Time Intelligence), Microsoft Purview, Azure Synapse Analytics, Azure Data Factory, and partner platforms such as Snowflake and Teradata. Partner with Architects on workload characterization, environment separation, capacity planning, and SKU sizing, balancing performance, scalability, resilience, and total cost of ownership. Share accountability with Architects for production architectures, owning performance outcomes, operational stability, and long‑term sustainability through continuous optimization and issue resolution. Data Engineering & Large Scale Data Processing Handson leadership in designing, building, and operating largescale batch and streaming data pipelines using Apache Spark, Databricks, Kafka, Hadoop, Hive, and HDInsight. Own data engineering standards, SLAs, SLOs, and performance baselines, while actively implementing and reviewing critical pipelines. Lead and perform pipeline performance assessments and tuning, including compute sizing, partition strategies, memory optimization, shuffle reduction, and concurrency management. Accountable for ensuring pipelines meet throughput, latency, and reliability targets in production environments. Drive DataOps practices with direct involvement in monitoring, alerting, capacity scaling, and continuous optimization. Database Platforms, Performance & Capacity Engineering Handson expertise across Azure SQL, SQL Server, PostgreSQL, MySQL, MariaDB, Oracle, Teradata, Netezza, Cosmos DB, and columnar analytics engines. Lead and execute database performance assessments, capacity planning, and workload sizing for transactional, analytical, and AI workloads. Perform query tuning, execution plan analysis, indexing strategy design, wait state analysis (e.g., CXPACKET), and storage/I/O optimization. Accountable for delivering predictable, scalable, and cost-efficient database performance in production. RealTime Analytics & Operational Intelligence Own the design and hands-on implementation of real-time analytics platforms using Microsoft Fabric RealTime Intelligence. Implement and tune Event streams, KQL databases, real-time dashboards, and ingestion pipelines to meet low latency and high throughput requirements. Take accountability for real-time workload sizing, Fabric capacity selection, ingestion rate planning, query concurrency, and retention strategies. Lead troubleshooting and optimization of production RTI workloads, balancing SLA targets with cost efficiency. Ensure real-time and historical datasets are unified in OneLake and ready for analytics and AI consumption. AI First Data Engineering & Unify Your Data Hands on ownership of AI first data engineering solutions, turning raw data into analytics ready and AI ready assets. Design and implement agentic AI workflows that assist in data discovery, preparation, profiling, validation, and performance optimization. Actively use large language models to automate data engineering tasks such as schema inference, pipeline generation, metadata enrichment, documentation, and tuning recommendations. Implement AI powered data wrangling solutions, while maintaining governance, explainability, and human in the loop controls. Accountable for operationalizing autonomous assisted data platforms in real customer environments. Lead the use of AI assistants (agents) to quickly build and improve data pipelines. This includes automating tasks like generating code for data processing, creating tests for quality, and helping to move data from older systems to newer platforms like Microsoft Fabric. The focus is on enabling teams to deliver faster and more reliably. AI & Advanced Analytics Enablement Own the hands-on integration of AI and ML workloads into data platforms, including performance tuning and cost optimization. Design, implement, and optimize solutions using AI Agents where viable using Azure Machine Learning, Azure OpenAI, and Azure AI Services, including RAG pipelines and inference workloads. Ensure AI and analytics solutions are grounded in Purview managed metadata and Business Glossary context, improving trust, explainability, and relevance of AI outputs. Apply Responsible AI principles by leveraging Purview classification, lineage, and sensitivity labels to control data access, usage, and model inputs in production AI systems. Tune AI systems for retrieval latency, inference performance, concurrency, and cost, using telemetry and real usage patterns. Embed observability, monitoring, drift detection, and performance metrics into production AI systems. Ensure Responsible AI principles are applied practically in live solutions. Design and model cost-efficient AI architectures, balancing performance/latency against consumption costs (e.g., Token optimization, SKU selection, Provisioned vs. Pay-as-you-go) Implement FinOps governance for AI, establishing budget guardrails, chargeback models, and ROI forecasting for high-consumption workloads. Data Governance, Trust & Operating Models Own the hands-on implementation of enterprise data governance using Microsoft Purview, including data cataloging, lineage tracking, classification, sensitivity labeling, and access policy enforcement. Lead the definition, rollout, and governance of an enterprise Business Glossary, establishing shared business definitions, ownership models, stewardship workflows, and lifecycle management. Ensure Business Glossary terms are mapped to physical data assets (tables, columns, streams, and semantic models) to bridge business and technical understanding across the organization. Enable business friendly data discovery by integrating Purview Catalog, lineage views, and glossary context into analytics, Fabric workloads, and AI solutions. Balance governance, performance, and usability by implementing governance by default patterns that scale self-service access without introducing friction. Support data mesh and domain-oriented ownership models using Purview as the central control plane for federated governance, standards, and policy enforcement. Industry & Multi-Cloud Experience Handson delivery ownership across industries including financial services, healthcare, manufacturing, retail/supply chain, energy, transportation, public sector, and media. Primary depth on Azure, with practical implementation experience on AWS and Google Cloud. Solutioning, Pre-Sales & Technical Leadership  Lead technical solutioning, estimations, and support architecture design during pre-sales and delivery shaping  Translate business outcomes into clear architecture, execution plans, and risk mitigation strategies  Influence technical direction across multiple teams and engagements  Act as a trusted advisor to clients and internal leadership  Frontier Engineering & Customer Enablement Skills  Lead rapid prototyping, pilots, and early-stage deployments for enterprise customers  Ability to go from whiteboard to production with minimal friction  Strong troubleshooting and production support expertise, including performance, reliability, and security issues  Side by side coding with customer engineering teams to unblock deployments and accelerate time to value  Outcome driven engineering focused on business impact, not activity  Building reusable assets, accelerators, and reference implementations  Comfortable operating in ambiguous, high-pressure environments with senior customer stakeholders  Certifications (Preferred) Two or more of the following: Microsoft Certified: Fabric Analytics Engineer Associate (DP‑600) Microsoft Certified: Azure Solutions Architect Expert (AZ‑305) Microsoft Certified: Azure AI Engineer Associate (AI‑102) Nice To Have Microsoft Certified: Information Protection and Compliance Administrator Associate (SC400) Microsoft Certified: Azure Machine Learning Engineer Associate (DP100) Microsoft Certified: DevOps Engineer Expert (AZ400) Confluent Certified Developer for Apache Kafka (CCDAK) Databricks Data Engineer Associate / Professional Certified Kubernetes Application Developer (CKAD) Microsoft Certified: Security, Compliance, and Identity Fundamentals (SC‑900) GitHub Certified: GitHub Copilot Professional (GHCP / GH‑300) Personal Attributes Passion for technology and innovation. Ability to interact confidently with senior leaders and clients. Strong decision-making skills and a consultative mindset. Flexibility to manage a fast-moving, ambiguous consulting environment. Commitment to continuous learning and professional growth. Other  Embody our culture and values  This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. 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