EY - GDS Consulting - AI And DATA - Snowflake plus AWS - Manager

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.

Lead - Data Engineering & AI Enablement (Life Sciences) Position Summary We are seeking a highly experienced and hands-on Data Engineering Leader with 12+ years of experience designing, building, and modernizing enterprise-scale data platforms. The ideal candidate will possess deep expertise across cloud-native data engineering, AI-enabled data modernization, and large-scale analytics platforms, with a strong preference for Life Sciences and Clinical Research domain experience. This role requires a blend of technical leadership, architecture design, hands-on development, delivery oversight, and client engagement. The candidate will be responsible for leading global engineering teams while actively contributing to architecture, solution design, implementation, optimization, and AI-driven automation initiatives.   Key Responsibilities

Lead end-to-end design, architecture, and implementation of modern cloud-native data platforms and analytics ecosystems. Design and develop scalable data ingestion, transformation, orchestration, and consumption frameworks using AWS, Snowflake, Python, and PySpark. Modernize legacy ETL and data warehouse solutions through automation, cloud migration, AI adoption, and platform optimization initiatives. Architect enterprise data solutions supporting structured, semi-structured, and unstructured datasets. Drive implementation of AI/ML and GenAI-enabled capabilities to improve pipeline efficiency, data quality, observability, and operational effectiveness. Partner directly with clients to understand business needs, define solution roadmaps, lead workshops, and provide technical advisory services. Lead distributed teams of data engineers, architects, and analysts while remaining actively involved in development and code reviews. Establish engineering best practices including DevOps, CI/CD, testing, security, monitoring, and governance standards. Collaborate with business and clinical stakeholders to translate requirements into scalable technical solutions. Drive solution estimation, planning, risk management, and delivery governance. Mentor team members and foster a culture of technical excellence and innovation.

Required Technical Skills Data Engineering & Programming

Python PySpark / Apache Spark Advanced SQL Data Modeling ETL / ELT Design Patterns Data Quality Frameworks Metadata-Driven Architectures API based Integration

Cloud & Modern Data Platforms

AWS (Strong expertise across core services including S3, Redshift, EMR, Athena, Kubernetes, Docker, Airflow, Bedrock) Snowflake Dataiku (nice to have) PostgresSQL

DevOps & Deployment

Git Jenkins CI/CD Pipelines Infrastructure as Code Automated Testing & Deployment Environment Management & Release Governance

AI & Automation

AI-enabled Data Engineering Pipeline Automation Data Observability GenAI Adoption for Data Operations Intelligent ETL Optimization Automated Data Quality & Monitoring Frameworks

Domain Experience (Highly Preferred)

Candidates with Life Sciences, Clinical Development, Biopharma, or Healthcare experience will be strongly preferred. Experience working with one or more of the following domains: Clinical Trial Data Protocol Data Participant/Subject Data Study Operations , Case Report Forms Central Laboratory Data Sample Lifecycle Management Biomarker & Assay Data Clinical Research Platforms CDISC / SDTM Standards

Leadership & Client Management

Proven experience leading teams of 10+ engineers across multiple workstreams. Strong stakeholder management and executive communication