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

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.

EY-Consulting - Data and Analytics - AI Data Platform Engineer - Databricks - Senior   EY's Consulting Services is a unique, industry-focused business unit that provides a broad range of integrated services that leverage deep industry experience with strong functional and technical capabilities and product knowledge. EY's financial services practice provides integrated Consulting services to financial institutions and other capital markets participants, including commercial banks, retail banks, investment banks, broker-dealers & asset management firms, and insurance firms from leading Fortune 500 Companies. Within EY's Consulting Practice, Data and Analytics team solves big, complex issues and capitalise on opportunities to deliver better working outcomes that help expand and safeguard the businesses, now and in the future. This way we help create a compelling business case for embedding the right analytical practice at the heart of client's decision-making.   Role AI Data Platform Engineer - Databricks Experience Guide

5-10 years Primary Skill Area Genie, AI/BI, Mosaic AI, Unity Catalog & Modern Data Platforms

The opportunity Build and operate enterprise-scale Data & AI platforms leveraging Databricks Lakehouse architecture. The role focuses on scalable data engineering, reusable platform engineering, governed self-service analytics, AI-enabled data products, APIs, enterprise service integration, Git-based delivery, Data SRE, data security, Immuta-style governance, and agentic automation using Databricks-native services including Genie, AI/BI, Mosaic AI, Unity Catalog, Vector Search, Lakeflow, and Delta Lake.   Your key responsibilities Databricks Data Engineering

Design and implement scalable ETL/ELT pipelines using PySpark, Spark SQL, Delta Lake, Databricks Workflows, Lakeflow, and Delta Live Tables. Develop reusable ingestion frameworks supporting batch, streaming, event-driven, CDC, file-based, and API-based processing patterns. Build curated, analytics-ready, and AI-ready data products with strong quality, lineage, semantic context, and operational controls. Optimise workloads for performance, cost, cluster/serverless usage, storage layout, and reliability.

Lakehouse & Platform Engineering

Create reusable platform accelerators for workspace onboarding, pipeline templates, deployment standards, logging, monitoring, and support runbooks. Implement Git connectivity, branching strategy, pull requests, code reviews, CI/CD, deployment bundles, and controlled environment promotion. Integrate Databricks with enterprise APIs, source systems, orchestration platforms, governance tools, security services, and downstream analytics consumers. Contribute to architecture reviews, technical design documentation, release management, and platform operations.

Genie, AI/BI & Agentic Enablement

Configure and manage Databricks Genie Spaces, AI/BI dashboards, and governed natural-language analytics over trusted data products. Enable Mosaic AI, MLflow, Vector Search, RAG, GraphRAG, agentic workflows, semantic retrieval, and AI-ready data products. Apply AI-assisted operations for schema drift detection, anomaly detection, pipeline failure diagnosis, automated documentation, and data quality recommendations.

Governance, Security & Data SRE

Implement Unity Catalog governance including RBAC/ABAC, lineage, audit logging, data masking, privacy controls, policy enforcement, and AI governance. Integrate with Immuta, Microsoft Purview, IAM, secrets management, monitoring tools, and enterprise access workflows. Build Data SRE capabilities covering