Senior Biomedical Data Manager

Role Overview

• Partners with scientists, engineers, and community collaborators to curate and coordinate multimodal biomedical datasets.
• Guides researchers through data ingestion into data hubs.
• Supports the development of data models and annotations for specialized biomedical data types.
• Contributes to data governance and workflow design that enable discovery-driven science.
• Applies metadata standards and contributes to improving data management plans across research projects.
• Independently manages datasets and ensures appropriate metadata documentation and lifecycle management.
• Identifies and resolves inconsistencies to validate datasets and metadata and ensure research readiness.
• Applies tools and automation techniques, including AI to improve efficiency of data management tasks such as metadata extraction, validation, and documentation.
• Coordinates with engineering, governance, and scientific teams to implement consistent data standards across projects.
• Provides guidance on data-sharing tools, standards, and best practices for contributors.

Requirements

• 5+ years of experience in biomedical data management/biocuration with a Bachelor’s degree in biomedical research, health informatics, health information management, library science, genomics, neuroscience, or a related discipline.
• Familiarity with biomedical data management/biocuration and an advanced degree (Master’s or PhD) in biomedical research, health informatics, health information management, library science or a related discipline.
• Experience working with complex multi-omic data (e.g., transcriptomics, whole genome sequencing, proteomics, metabolomics, epigenetics, clinical/phenotypic data).
• Experience working on multidisciplinary teams.
• Experience with biomedical data models, metadata harmonization, and ontology development or refinement.
• Experience using project tracking tools such as Jira.
• Familiarity with multiple disease domains (e.g., rare disease, neurodegenerative disease, cancer, immunology).
• Proficiency in data management tools and techniques.
• Strong organizational and time-management skills.
• Clear and effective written and verbal communication skills.
• Demonstrated critical thinking and thoughtful, inclusive technical practices.
• Curiosity about and critical assessment of AI tools to support data management and analysis work.
• Familiarity with using code to manage data and use of version control systems.

Benefits

• Health & Wellness: Comprehensive medical, dental, vision, life, AD&D, and long-term disability.
• Future Security: Robust retirement plan and flexible spending accounts (FSA).
• Work-Life Harmony: Paid time off and flexible work arrangements.