Serve as primary point person between Data Team and Data Acquisition to coordinate receipt of hospital files, validation, and updates to files as needed. Develop and implement validation workflows involving cross functional teams to assess new data feeds and files to ensure accuracy and consistency between the acquired data and source systems, such as client hospital EMR systems or sources internal to the organization. Validate data accuracy by working closely with Subject Matter Experts or Medical Directors and their Site Leadership Teams to reconcile data discrepancies and guide requests to completion. Create training content and provide centralized guidance for cross functional team members on their roles in validation to ensure accurate reporting. Collaborate with Data Engineering to assess data findings from quality checks and anomaly detection, conduct initial troubleshooting, and escalate problems to appropriate teams when necessary. Work with data team to track and report on data completeness and quality metrics, including the fill rates of key data elements from source systems, and escalate for individual sources or clients when certain thresholds are met. Document data quality issues and troubleshooting efforts as incidents and tickets in ServiceNow and/or Jira, ensuring detailed records of incidents and tracking of resolutions. Communicate issues, blockers, or changes to data feeds to downstream parties or users and ensure timely resolution or escalation when necessary. Continuously improve data quality standards, processes, and workflows to ensure data accuracy across sources. Lead and participate in cross-functional data quality improvement projects aimed at enhancing the integrity of the data in the Enterprise Data Warehouse and its downstream products. Collaborate with internal stakeholders, including Product Owners/Program Managers, Data Engineers, and Data Acquisition teams to define data quality project objectives, timelines, and deliverables. Design and implement initiatives to automate and streamline data quality monitoring and validation processes, reducing manual effort and increasing overall efficiency. Provide regular project updates, documentation, and outcomes to senior leadership, offering data-driven insights and recommendations for further improvements.