Join EY and contribute to building an exceptional career experience while helping to create a better working world. EY's Consulting Services is a dedicated unit focused on integrated services for financial institutions and capital markets participants. Within this practice, the Data and Analytics team tackles complex issues and capitalizes on opportunities to deliver enhanced business outcomes. We help create a compelling business case for integrating advanced analytical practices into core client decision-making processes.

This role focuses on developing and operating enterprise-scale Data & AI platforms leveraging the Databricks Lakehouse architecture. Key areas include scalable data engineering, reusable platform engineering, governed self-service analytics, AI-enabled data products, API integrations, and robust Git-based delivery practices.

Design and implement scalable ETL/ELT pipelines using PySpark, Spark SQL, Delta Lake, and Databricks native services. Develop reusable ingestion frameworks for diverse processing patterns and build high-quality, AI-ready data products with strong lineage and operational controls.

Create platform accelerators for workspace onboarding, pipeline templating, and deployment standards, implementing Git workflows, CI/CD, and controlled environment promotions. Integrate Databricks with enterprise systems, APIs, and downstream consumers, contributing to architecture reviews and release management.

Configure and manage Databricks Genie Spaces, AI/BI dashboards, and natural-language analytics. Enable advanced AI capabilities such as Mosaic AI, RAG, and agentic workflows, applying AI-assisted operations for schema drift, anomaly detection, and automated documentation.

Implement comprehensive Unity Catalog governance, including RBAC/ABAC, lineage, and AI governance. Integrate with security and governance tools like Immuta and Purview, and build Data SRE capabilities for observability, incident management, and production readiness.

We seek a skilled engineer with 5-10 years of experience in data engineering, data platform operations, analytics engineering, or AI platform enablement. A strong foundation in cloud data platforms, API integration, Git, CI/CD, governed access, SRE practices, and production operations is essential.

Ideal candidates possess architecture awareness, a delivery ownership mindset, and a platform engineering perspective. Comfort in translating platform standards into reusable frameworks, secure implementations, and production-ready services is expected. The ability to mentor engineers, collaborate effectively, and adopt AI-native engineering methods is also highly valued.