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Tier-1 employer and metro location increase competition, but niche clinical and AI/LLM requirements reduce applicant pool.
Strong clinical trial domain and GxP requirements limit transferability across industries.
Extensive mandatory clinical, GxP, and specialized data/AI technology requirements enforce strict candidate filters.
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Lead design, build, and maintenance of scalable, large-scale data pipelines and infrastructure for Clinical Trial Execution workflows.
Independently execute data engineering projects including ETL/ELT pipelines, data architecture optimization, and data quality management.
Collaborate with business units and data scientists to translate clinical data needs into technical solutions, ensuring GxP compliance and high data reliability.
Experience with big data technologies like Spark, Python or Scala programming, and expert SQL skills.
Deep understanding of Clinical Trial Execution workflows and related operational data systems (CTMS, EDC, IxRS).
Experience managing data products with compliance to GxP standards and stakeholder management in agile, product-led teams.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in implementing advanced data architectures including Data Mesh, semantic modeling, knowledge graphs, and ontologies.
Demonstrated ability working with AI/LLM infrastructures such as Retrieval-Augmented Generation pipelines, vector databases, and agentic AI frameworks.
Skilled at bridging technical and non-technical stakeholders, particularly in clinical domains, to translate complex AI and data requirements into operational solutions.