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

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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 skills.
• Ability to independently lead client meetings, workshops, solution reviews, and architecture discussions.
• Experience managing onsite/offshore delivery models.
• Demonstrated ability to influence technical and business decision-making at senior leadership levels.

 

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Data Science, or related discipline.
• 12+ years of progressive experience in Data Engineering, Cloud Data Platforms, and Analytics Solutions.
• Experience leading large-scale transformation and modernization programs.
• Cloud certifications (AWS, Snowflake, AI/ML) are highly desirable.

 

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