EY - GDS Consulting - AI And DATA - AWS Databricks - Senior

 Data Engineer Life Sciences R&D (Senior Level) 
 Experience8+ Years 
 Role Overview 
As a senior Data Engineer, you will be responsible for building robust, scalable, and compliant data pipelines from raw scientific sources to integrated and consumption-ready data products in the R&D landscape. You will partner with architects, domain SMEs, and analysts to develop re-usable components supporting early discovery, development, and regulatory needs.  Key Responsibilities 

Design and implement pipelines across L1L3 layers using Databricks and Spark.

Develop modular, parameterized ETL processes for large-scale scientific data integration.

Implement and maintain STTM mappings and transformation logic with domain BAs.

Build metadata-driven orchestration, logging, and observability frameworks.

Support performance optimization, validation, and deployment readiness across squads.

Collaborate with architects on cloud-native best practices and standards.

 Must-Have Skills 

8+ years of experience in data engineering, with 3+ in life sciences or R&D settings.

Hands-on expertise with Spark, Databricks, Delta Lake, and Python/SQL.

Experience in building pipelines with STTM/SDTM alignment and scientific data quality requirements.

Good understanding of L1L3 pipeline layering and governance controls.

Familiarity with GxP or other compliance-driven implementations.

 Nice-to-Have Skills 

Exposure to ELNs, LIMS, compound registration systems, or bioassay platforms.

Knowledge of Unity Catalog, Matillion, or metadata-driven architecture approaches.

Experience with ingestion frameworks, schema evolution, and CI/CD for data.

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