Principal Data & AI Consultant

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. 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. 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. 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. 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. 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. 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 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 customers Two or more of the following: Confluent Certified Developer for Apache Kafka (CCDAK) Databricks Data Engineer Associate / Professional Certified Kubernetes Application Developer (CKAD) GitHub Certified: GitHub Copilot Professional (GHCP / GH‑300) Ability to interact confidently with senior leaders and clients. Ensure adherence to coding standards, architectural integrity, and performance benchmarks. 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. Ensure secure, compliant, and reliable solution delivery through secure coding, test driven development, observability, design reviews, and quality gates across all engagements. Provide strategic guidance and execution oversight to ensure alignment with organizational goals. Perform query tuning, execution plan analysis, indexing strategy design, wait state analysis (e.g., CXPACKET), and storage/I/O optimization. Implement and tune Event streams, KQL databases, real-time dashboards, and ingestion pipelines to meet low latency and high throughput requirements. Ensure real-time and historical datasets are unified in OneLake and ready for analytics and AI consumption. Design and implement agentic AI workflows that assist in data discovery, preparation, profiling, validation, and performance optimization. Implement AI powered data wrangling solutions, while maintaining governance, explainability, and human in the loop controls. 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. 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. 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. Support data mesh and domain-oriented ownership models using Purview as the central control plane for federated governance, standards, and policy enforcement.