Manager, Clinical Data Innovation

Job Description:

Responsibilities

• Support the development and maintenance of comprehensive architecture strategies tailored to the needs of managing and transferring clinical trial data, from research and collection to submission.
• Research and implement innovative data technologies and methodologiesfor Master Data Management and Data Quality (e.g. Reltio, Data Quality checks etc.)
• Develop and implement data strategies to ensure the accuracy, integrity, and security of clinical data.
• Provide technical support and guidance to users as needed.
• Collaborate closely with other DigitalX team members and data professionals to integrate assigned systems or environments with other systems and workflows, ensuring a smooth and controlled flow of data.
• Support designing scalable architectures to track and maintainproduct data and associated automated jobs.
• Oversee and support development of APIs, interfaces, and middleware solutions for seamless communication and data exchange between clinical software systems and databases.
• Lead or take part in projects related to system upgrades, migrations, or new implementations.
• Liaise with software vendors and service providers to address issues, manage licenses, and support negotiate contracts.
• Actively participate in or lead Agile activities, including backlog grooming, using Azure DevOps Services.
• Stay updated on industry guidelines (e.g. IDMP compliance) and advancements to recommend and implement new tools and practices.
• Responsible for Data Quality (DQ) business rules implementation, DQ Analysis, and DQ Remediation activities.
• Identify, document and track DQ violations and defects, work with development, data lake teams to resolve issues.
• Assistorganizations stay ahead of data trends and discover new ways to useproduct data.

Quaification & Experience

• Bachelor of Science degree in Computer Science, Information Systems, Data Science, or a related field.
• Minimum of 5 years of relevant experience working in Master Data Management solutions and Data Quality framework within a healthcare industry.
• Deep understanding of clinical data systems, as well as related environments and tools.
• Proficient in Reltio data model configuration (Entity types, attributes), matching rules, operational values and survivorship rules, relationships, User Interface, and hierarchies.