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 (Solid 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. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.

Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

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