Principal Data Analytics Engineer

Overview

The Principal AI & Semantic Engineer is the senior technical authority connecting governed enterprise data and business meaning with scalable analytics and AI solutions, defining architecture, standards and engineering patterns for semantic models, governed metrics, AI-ready data products, MLOps and generative AI consumption.

What you'll do

• 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 ML and 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.

What you'll need

• 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.

Nice to have

• 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.

Details

• Location: Bangalore, India.