• Manage Projects & Technology • Lead and implement Data Receipt Agreements with vendors in collaboration with cross-functional teams • Program and establish import procedures for data ingestion using SAS or alternative technologies (e.g., Workbench) • Design and implement reconciliation checks to ensure accurate data transfer • Program offline listings and custom reports to provide valuable insights on external data • Aggregate data across all sources and manage data structures, missing values, and programming errors • Review data outputs and provide strategic insights to study teams and clients • Ensure first-time quality on all deliverables • Negotiate electronic data timelines and ensure adherence through active project management • Monitor project resourcing, identify scope changes, and resolve technical issues • Coordinate and lead programming teams to successful project completion within timelines and budget • Manage deployment of data management technology for offline listing creation • Act as SME and technology owner for data management offline listing platforms • Maintain comprehensive supporting documentation in accordance with SOPs, Guidelines, and Work Instructions • Ensure traceability and regulatory compliance across all study activities • Document deviations and communicate them to project teams • Support Initiatives & Continuous Improvement • Participate in creating standards through tools (SAS macros), libraries, and processes • Develop and implement project-specific tools and improvements • Lead or drive global initiatives related to processes and new technologies • Mentor staff and provide relevant training • Assist project teams in problem resolution and technical support • Maintain and expand regulatory knowledge within the clinical research industry • Serve as point of contact for clients and internal stakeholders on electronic data matters • Participate in bid defense meetings • Independently contribute ideas on technology and data engineering to support business development

• Explore legacy source systems to understand their structure, quality, and relationships. • Export source data across a range of access methods. • Transform exported data into gaiia's standard format. • Execute migrations end-to-end on the internal migration platform. • Build and reuse mappings, templates, and validation rules. • Contribute small engineering improvements to the migration tooling. • Diagnose and resolve data discrepancies during dry runs, UAT, and post-cutover. • Document mapping specs, transformation logic, and data anomalies.

• Define and execute the enterprise data engineering strategy supporting business growth, operational excellence, analytics, and AI initiatives • Establish enterprise standards for data ingestion, integration, transformation, storage, quality, and delivery • Develop a scalable data architecture capable of supporting a rapidly growing and acquisition-driven organization • Create and maintain a multi-year roadmap for enterprise data capabilities and platform investments • Lead the design, implementation, and operation of the company’s enterprise data platform and lakehouse architecture • Establish scalable patterns for batch, real-time, and event-driven data integration • Partner with Data & Information Architects to maintain enterprise data models and architecture standards • Develop repeatable integration frameworks for newly acquired firms and business units • Partner with the Director of Data & AI Governance to operationalize governance controls within enterprise platforms and workflows • Recruit, develop, and lead a high-performing team of Data Engineers, Data & Information Architects, and Governance professionals.

• Design, develop, and maintain scalable data pipelines that support enterprise reporting, analytics, and AI workloads. • Integrate data from a variety of business systems including ERP, CRM, HRIS, Deltek Vantagepoint. • Develop ingestion, transformation, and data delivery processes that support enterprise business requirements. • Build and maintain data workflows that support both batch and near real-time processing needs. • Ensure data pipelines are reliable, maintainable, and scalable as business demands evolve. • Implement data quality controls, validation processes, and monitoring solutions. • Collaborate with Data & Information Architects to implement enterprise data models and master data strategies. • Support data classification, retention, lineage, access management, and audit requirements.