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Tier-1 brand, popular mid-level data engineer title, metro location, and broad multi-skill requirements.
Strong clinical and biomedical knowledge-graph requirements make background fit highly industry-specific and less transferable.
Multiple mandatory technical skills and an explicit 5-8 years requirement enforce high shortlisting strictness.
Build and maintain data ingestion pipelines and ETL/ELT workflows for clinical trial documentation (protocols, ICF, SmPCs, CSRs, published articles).
Design and implement relational (Amazon Aurora) and graph database (GraphDB) data models to represent complex clinical trial entities and relationships.
Develop embedding/vectorization pipelines and APIs to support AI-driven clinical trial study design insights with regulatory traceability.
Bachelor's degree in Computer Science, Data Engineering, or related discipline.
5-8 years experience building production-grade data platforms and pipelines.
Strong skills in Python (including PDF/document parsing libraries), advanced PostgreSQL-compatible SQL, and graph databases (Neptune, Neo4j, or similar).
Experience with AWS services (Aurora, S3, Lambda, Step Functions), NLP/document processing, and pipeline orchestration tools (Airflow, Prefect, or similar).
Experienced in biomedical knowledge graphs, clinical trial data structures, and related APIs (PubMed, ClinicalTrials.gov, EMA/CTIS).
Technical ability to implement complex data workflows combining relational and graph stores with AI/ML data integration for clinical research.
Comfortable working at the intersection of clinical science, data engineering, and AI-driven product delivery in a regulated environment.