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Enterprise Architect - Life Sciences R&D

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Onsite

Enterprise Architect - Life Sciences R&D

ZS

Enterprise Architect - Life Sciences R&D

Skills

Data EngineeringDevOpsRSnowflakeDatabricksData AnalysisMachine LearningAIAWSHadoopComplianceAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Gurugram, Haryana, India

Work mode:

Onsite

Published on:

17 August 2026

What they need


In this role, you will be aligned to Life Sciences RD team of ZS working across multiple clients in designing and developing cutting edge RD solutions which will help clients to discover and develop innovative medicines that improve patients lives.

The ideal candidate will have deep expertise in architecting solutions for study design, feasibility, custom clinical data repositories (CDRs), statistical computing environments (SCEs), and advanced clinical analytics platforms such as RBQM and Control Towers. Experience with systems integration (SI), cloud architecture (AWS/Azure), and a solid understanding of the clinical/RD domain is essential.

What Youll Do:

  • Lead the end-to-end architecture and technical design of clinical and RD IT solutions, including study design, feasibility tools, SCEs, and custom CDRs.
  • Drive the architecture of advanced analytics platforms like Risk-Based Quality Management (RBQM), clinical control towers, and real-time data insights systems.
  • Collaborate cross-functionally with Clinical Operations, Biostatistics, Data Management, and external vendors to align technology solutions with business goals.
  • Design robust and scalable solutions consistent with ZS and industry practices; take advantage of the existing assets and maintain a balance between architecture requirements and specific client needs.
  • Define and maintain enterprise and solution architecture standards, ensuring alignment with security, scalability, and compliance requirements.
  • Develop AI based agentic architectures for supporting solution need e.g. AI Agent based content authoring
  • Oversee systems integration between EDC, CTMS, eTMF, CDR, and downstream analytics platforms.
  • Serve as the architectural lead on custom-built applications and digital initiatives in the clinical data management and analytics space.
  • Ensure architectural compliance with regulatory frameworks (e.g., GxP, 21 CFR Part 11).
  • Create architecture artifacts: solution blueprints, reference architectures, integration diagrams, and technical roadmaps.
  • Collaborate with security and infrastructure teams on cloud-native deployments (AWS or Azure) and DevSecOps strategies.
  • Mentor technical teams and support architectural governance across project delivery.

What Youll Bring:

  • Bachelor''s degree with specialization in Computer Science, IT or other computer related disciplines;
  • 12+ years of relevant experience in designing developing technology solutions on an enterprise-scale for Life Science RD space;
  • Strong knowledge of clinical systems landscape (e.g., EDC, CTMS, eTMF, eCOA, CDMS, SDTM, ADaM, SAS).
  • Expertise in cloud architecture using AWS and/or Azure including serverless, containers, API management, and security best practices.
  • Deep understanding of integration patterns, data pipelines, and real-time analytics architecture.
  • Experience with data governance, metadata management, and compliance standards in life sciences.
  • Specializes in architecting modern data ecosystems leveraging technologies such as Azure, AWS, Databricks, Snowflake, and Hadoop
  • Demonstrated expertise in building scalable data pipelines, real-time streaming architectures, and enterprise-grade data lakes and warehouses.
  • Known for cross-functional leadership in aligning business needs with technical roadmaps, enabling analytics, AI/ML, and decision intelligence.
  • Skilled in Agile delivery models, stakeholder management, and building Centres of Excellence (CoEs) to drive innovation in data engineering practices.

Additional Skills

  • Thought leadership in the form of articles, blogs, forums, papers etc. on data engineering will be preferred.
  • Experience in key components like data governance curation, data quality and master data management, data integration, data virtualization, self-service interaction for data preparation and testing is a plus.

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