Principal Data Analytics Engineer

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The Principal AI Semantic Engineer is the senior technical authority responsible for connecting governed enterprise data and business meaning with scalable analytics and artificial intelligence solutions. This role defines the architecture, standards, and engineering patterns for semantic models, governed metrics, AI-ready data products, machine learning operations, and generative AI consumption.

Operating at the intersection of Analytics, Data Engineering, Data Science, Governance, and AI Platform Engineering, this role ensures that enterprise data, metrics, models, and knowledge assets can be reliably discovered, understood, deployed, monitored, and reused. The Principal AI Semantic Engineer provides hands-on technical leadership, guides complex implementations, and mentors engineers and analysts while remaining an individual contributor.

• 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.

Evaluate emerging technologies and recommend pragmatic adoption paths aligned with enterprise architecture and governance standards.

• 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.