EY - GDS Consulting - AI And DATA -AI Data Platform Engineer

At EY, you ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

Role

AI Data Platform Engineer - MS Fabric

Experience Guide

5-10 years

Primary Skill Area

Microsoft Fabric, OneLake, Lakehouse, Data Factory, Power BI Semantic Models AI-Ready Data Products

The opportunity

Build and operate enterprise-scale Data AI platforms using Microsoft Fabric and the Azure data ecosystem. The role focuses on Fabric Lakehouse, Warehouse, OneLake, Data Factory pipelines, notebooks, Power BI semantic models, reusable platform engineering, APIs, Azure service integration, Git connectivity, Data SRE, data security, Immuta/Purview governed access, and AI/agentic enablement for modern analytics and GenAI workloads.

Your key responsibilities

• Microsoft Fabric Data Engineering
  • Design and implement Fabric data solutions using OneLake, Lakehouse, Warehouse, Data Factory Pipelines, Dataflows, Notebooks, Spark, SQL endpoints, and Power BI semantic models.
  • Build reusable ingestion and transformation frameworks for batch, event-driven, API-based, file-based, and enterprise application integration patterns.
  • Develop curated data products across raw, standardised, trusted, and consumption layers with quality, lineage, and operational controls.
  • Optimise pipeline performance, workspace usage, refresh patterns, capacity consumption, and downstream analytics readiness.
• Fabric Azure Platform Engineering
  • Create reusable platform standards for workspace onboarding, deployment pipelines, item naming, logging, monitoring, semantic model patterns, and support runbooks.
  • Implement Git connectivity, branching strategy, pull requests, code reviews, CI/CD, Fabric REST API automation, and environment promotion.
  • Integrate Fabric with Azure Data Lake Storage, Event Hubs, Functions, Key Vault, Azure DevOps, Azure Monitor, Microsoft Purview, Synapse, Databricks, and enterprise APIs.
  • Support platform administration, release management, governance readiness, and production operations.
• AI, Copilot Agentic Enablement
  • Enable Fabric Copilot, AI-ready data products, semantic models, RAG-ready datasets, metadata-driven automation, and conversational analytics use cases.
  • Support AI integration across Power BI, semantic layers, enterprise search, GenAI assistants, and downstream data consumers.
  • Apply agentic operations for schema drift detection, failed pipeline summarisation, root-cause recommendation, data quality anomaly detection, and automated documentation.
• Governance, Security Data SRE
  • Implement Entra ID, workspace roles, item permissions, sensitivity labels, lineage, auditability, Key Vault integration, masking/security patterns, and controlled access.
  • Integrate with Microsoft Purview, Immuta, Collibra, IAM, monitoring services, data quality tools, and enterprise access workflows.
  • Build Data SRE dashboards for Fabric capacity, workspace usage, pipeline failures, refresh performance, data quality, cost, access activity, and SLA/SLO adherence.

Skills and attributes for success

Skill / capability area

• Core platform: Microsoft Fabric, OneLake, Lakehouse, Warehouse, Data Factory Pipelines, Dataflows, Notebooks, Spark, Power BI semantic models.
• AI and GenAI: Fabric Copilot, AI-ready data products, semantic models, RAG, enterprise search, metadata automation, Agentic AI.
• Engineering: Python, PySpark, SQL, APIs, Git, CI/CD, Delta/Parquet, unit testing, integration testing, data pipeline testing.
• Azure and DevOps: Azure DevOps, Entra ID, Key Vault, Event Hubs, Functions, Azure Monitor, Purview, ADLS, deployment pipelines, policy-as-code.
• Governance and reliability: Purview, Immuta, sensitivity labels, lineage, audit, masking/security patterns, data quality, observability, Data SRE, FinOps.

To qualify for the role, you must have

• 5-10 years of experience in data engineering, data platform operations, analytics engineering, platform engineering, or AI platform enablement.
• Strong hands-on implementation experience with cloud data platforms, APIs, Git connectivity, CI/CD, governed access patterns, SRE practices, and production operations.
• Preferred certifi…