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Tier-1 brand, generic Data Engineer title, and metro Hyderabad increase applicant competition.
Specialized clinical biomarker, CDISC, and assay experience creates strong domain bias, reducing cross-industry transferability.
Explicit 8+ years, 3+ years domain experience, and many mandatory technical and domain skills make filters strict.
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Design, build, test, deploy, and maintain end-to-end data ingestion pipelines for biomarker and clinical data supporting clinical trials.
Automate data validation, quality control, error handling, and remediation workflows to ensure data quality and traceability.
Integrate agentic automation, Codex workflows, and generative AI to optimize turnaround times and efficiency; collaborate with labs, CROs, and vendors for assay onboarding and data standardization.
8+ years work experience with Bachelor's degree in Computational Biology, Bioinformatics, AI, Computer Science, Data Engineering, or related field; PhD is a plus.
3+ years experience in data or platform engineering roles; experience with biomarker/clinical data or clinical research environment highly desirable.
Strong Python programming, database design, and experience with Databricks.
Experience with clinical labs and biomarker assays (e.g., immunoassay, flow cytometry, proteomics, sequencing); familiarity with workflow tools (Airflow, Nextflow, snakemake) and cloud platforms (AWS).
Experienced in handling complex biomarker and clinical datasets within clinical research settings, with exposure to clinical labs and vendor collaborations.
Technically proficient in developing automated, scalable data pipelines using Python, workflow orchestration, AI-driven automation, and cloud infrastructure.
Skilled in data standardization, quality control, version control (Git), CI/CD, containerization (Docker), and compliant with clinical data standards (CDISC/SDTM/ADaM).