We are seeking an experienced and detail-oriented Clinical Data Manager to join our growing clinical operations team. This role is critical in ensuring the integrity, accuracy, and quality of clinical trial data, with a particular focus on studies in rare and orphan diseases. You will work cross-functionally with clinical, biostatistics, regulatory, and external vendors to manage data across all phases of clinical development. Key Responsibilities Lead and manage end-to-end data management activities for assigned clinical trials, from study start-up through database lock. Develop and review data management plans (DMPs), case report forms (CRFs), edit check specifications, and data validation rules. Oversee data cleaning activities, including query generation and resolution, discrepancy management, and data reconciliation. Collaborate with CROs and external vendors to ensure timely and high-quality data deliverables. Ensure compliance with regulatory requirements (e.g., ICH-GCP, CDISC standards) and internal SOPs. Participate in protocol review, CRF design, and database build processes. Support audits and inspections by regulatory authorities. Provide input into the development of rare disease-specific data strategies, including patient-reported outcomes and real-world data integration. Contribute to continuous improvement initiatives within the data management function. Qualifications and Experience Bachelor's degree in life sciences, health informatics, or a related field (Master's preferred). Minimum of 3-5 years of experience in clinical data management, with at least 1-2 years in rare disease or orphan drug trials. Strong understanding of clinical trial processes, data standards (CDISC, SDTM), and regulatory requirements. Proficiency with EDC systems (e.g., Medidata Rave, Oracle InForm, Veeva Vault). Experience working with CROs and external vendors. Excellent organizational, communication, and problem-solving skills. Ability to work independently and collaboratively in a fast-paced environment. Preferred Skills Experience with decentralized trials or real-world evidence (RWE) data. Familiarity with biomarker or genetic data integration. Knowledge of statistical programming languages (e.g., SAS, R) is a plus.