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Clinical Data Operations Manager
HybridFull-timeData EngineeringBiotechnology
Own the end-to-end intake, review, structuring, harmonization, quality control, and database integration of clinical data for an AI-driven drug discovery platform. Work across clinical, scientific, data, product, and engineering teams to build scalable workflows and improve operational efficiency.
Responsibilities
- Define clinical data needs for projects
- Own clinical data ingestion from initial review through database integration
- Work with partners to understand data structures, terminology, documentation, and gaps
- Translate clinical trial data into standardized formats for internal systems
- Maintain data quality, consistency, completeness, traceability, accessibility, and compliance
- Define ingestion requirements with data, research, product, and engineering teams
- Automate and streamline data review, mapping, harmonization, and quality-control workflows
- Develop scalable standards, templates, standard operating procedures, and best practices
- Troubleshoot data issues and resolve inconsistencies
- Bridge clinical and scientific stakeholders with technical and data teams
Requirements
- Experience with clinical trial metadata, clinical datasets, or patient-level clinical information
- Strong understanding of clinical trial structure, including indications, cohorts, arms, timepoints, treatments, response, safety, and outcomes
- Experience with databases, data dictionaries, ontologies, metadata schemas, or data harmonization workflows
- Familiarity with relevant regulations and quality standards
- Practical experience using AI tools to improve workflows, automate repetitive tasks, support data review, or increase efficiency
- Ability to manage complex information and translate clinical concepts into technical implementation requirements
- Strong cross-functional communication and collaboration skills
Nice to have
- Experience with CDISC, SDTM, OMOP, or related clinical ontologies
- Experience with SQL, Python, R, or other data manipulation tools
- Experience in biotech, pharmaceutical, clinical research, or computational biology environments
- Familiarity with real-world, translational research, omics, or multimodal clinical data
- Experience improving data ingestion pipelines, data models, or data platforms