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Tier-1 brand attracts applicants but niche clinical and ML infra requirements narrow candidate pool.
Requires deep clinical trial and GxP expertise, significantly limiting cross-industry portability.
Multiple mandatory specialized skills (clinical, GxP, RAG, data mesh) and seniority make shortlisting strict.
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Lead design, development, and maintenance of scalable data pipelines and infrastructure for clinical trial data management.
Independently execute end-to-end data engineering projects, solve complex data ingestion issues, and optimize data flows and system performance.
Collaborate with data scientists and clinical stakeholders to meet data needs, translating clinical operations requirements into technical solutions while managing large-scale data systems.
Proficiency in programming with Python or Scala and expert SQL knowledge.
Experience with big data technologies like Spark and cloud data architectures (e.g., data lakes, warehouses).
Deep understanding of Clinical Trial Execution workflows and operational data systems such as CTMS, EDC, and IxRS.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in AI/LLM infrastructure including RAG pipelines and agentic AI frameworks (LangChain, AutoGen, CrewAI).
Skilled in advanced data architecture concepts including Data Mesh, semantic data modeling, knowledge graphs, and GxP compliance.
Proven ability to work in product-led agile teams translating clinical business requirements into technical specifications and managing data product lifecycles.