Overview

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Responsibilities

• Define enterprise standards and reference patterns for semantic models, dimensional models, business metrics, KPIs, hierarchies, calculations, and reusable analytical assets.
• Architect semantic consumption patterns across Microsoft Fabric, Power BI, Databricks Gold-layer data, and approved AI applications.
• Partner with business domain owners, analysts, and Data Governance to translate business concepts into consistent, governed definitions and analytical models.
• Lead design reviews for semantic models and analytical data products, balancing usability, reuse, performance, security, and maintainability.
• Ensure semantic assets are documented with appropriate metadata, lineage, ownership, and change controls.
• Define engineering standards for deploying, versioning, testing, monitoring, and governing machine learning and generative AI solutions.
• Architect reusable MLand LLM patterns, including CI/CD, model and prompt versioning, experiment tracking, evaluation, observability, rollback, and production support.
• Guide implementation of feature pipelines, model registries, inference workflows, retrieval pipelines, vectorized knowledge assets, and AI evaluation frameworks.
• Partner with Data Scientists and AI product teams to move prototypes into secure, scalable, and supportable production services.
• Implement technical controls for traceability, reproducibility, model performance, data drift, responsible AI requirements, and operational reliability.
• Design curated, governed data products and knowledge assets for predictive analytics, generative AI, copilots, agents, and retrieval-augmented generation.
• Define how structured data, enterprise metrics, metadata, documents, embeddings, and retrieval indexes are linked and consumed.
• Ensure AI applications use approved business definitions, metrics, access policies, and authoritative data sources.
• Collaborate with Architecture and Governance teams on ontology, glossary, metadata, lineage, classification, and policy integration.
• Serve as the technical escalation point for complex semantic, analytics, MLOps, and AI engineering decisions.
• Create reference implementations, reusable frameworks, coding standards, and engineering playbooks.
• Mentor Senior Analytics & AI Engineers, Semantic Engineers, Data Analysts, and Data Scientists.
• Influence cross-functional roadmaps and communicate architecture decisions, trade-offs, risks, and business outcomes to technical and executive audiences.

Qualifications

• 10+ years of experience across analytics engineering, business intelligence, semantic modeling, data engineering, machine learning engineering, MLOps, or related disciplines.
• Demonstrated experience serving as a principal-level technical leader or architect for enterprise data, analytics, or AI solutions.
• Deep expertise in SQL, dimensional modeling, semantic model design, analytical performance optimization, and governed metric implementation.
• Hands-on experience with Microsoft Power BI and/or Microsoft Fabric semantic models, including DAX and enterprise-scale model design.
• Hands-on experience with Databricks or a comparable cloud data and AI platform, including production data pipelines and model lifecycle capabilities.
• Experience designing or operating CI/CD, experiment tracking, model registries, monitoring, evaluation, and observability for ML or generative AI solutions.
• Working knowledge of generative AI patterns such as embeddings, vector retrieval, RAG, prompt and model evaluation, and agent-based solutions.
• Strong understanding of metadata, lineage, data quality, access control, business glossary, and responsible AI principles.
• Proven ability to mentor technical professionals, lead architecture reviews, and influence stakeholders without direct authority.
• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field; equivalent practical experience will be considered.
• Experience with Microsoft Purview, Unity Catalog, MLflow, Azure DevOps or GitHub Actions, and cloud observability tooling.
• Experience supporting enterprise Finance, GTM, Product, Customer, or Workforce analytics domains.
• Experience establishing reusable semantic or AI platform capabilities in a federated operating model.