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

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Technical Architect – Life Sciences R&D (Manager Level)

• Experience: 10+ Years
• Employment Type: Full-Time

Role Overview

As a senior Technical Architect, you will drive the design and implementation of robust and scalable architecture across data ingestion (L1), integration (L2), and consumption (L3) layers for life sciences R&D. You will collaborate with domain experts, data engineers, and product stakeholders to ensure architecture is modular, reusable, and compliant with scientific and regulatory standards.

Key Responsibilities

• Own the end-to-end data architecture supporting R&D use cases—from early discovery through development and regulatory readiness.
• Define architecture patterns for L1–L3 layers in compliance with enterprise standards and scientific usability.
• Collaborate with data engineers to ensure alignment with STTM mappings, orchestration logic, and consumption layer requirements.
• Participate in design reviews, architecture validations, and tooling decisions across squads.
• Guide cross-functional teams on cloud-native best practices, metadata management, and platform modularity.
• Maintain architectural artifacts and contribute to documentation and technical enablement across teams.

Must-Have Skills

• 10+ years of experience in technical architecture or data engineering, with 3+ in life sciences or similarly regulated domains.
• Deep understanding of L1–L3 data flow concepts, source-to-target transformations, and modular pipeline design.
• Strong proficiency in Databricks, Spark, Delta Lake, and cloud-native technologies (Azure/AWS/GCP) AWS preferred.
• Exposure to SDLC in highly compliant settings (e.g., GxP, FAIR, 21 CFR Part 11).
• Hands-on experience with metadata lineage, data cataloging, and integration with enterprise governance frameworks.

Nice-To-Have Skills

• Knowledge of IDMP/RIMS standards, SDTM or STTM mappings, or other domain-specific data transformation frameworks.
• Familiarity with semantic and conceptual modeling in drug discovery or clinical settings.
• Experience with DevOps for data—CI/CD pipelines, containerization, and versioned releases.

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