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

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Data Engineer – Life Sciences R&D (Senior Level)

Experience: 8+ 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 L1–L3 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 L1–L3 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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