Role Description Assist direct manager in overseeing and leading clinical data abstractors and related operations. Partner with the Lead Quality Abstractor, and the team’s software engineers and data management specialists to operationalize end-to-end abstraction, clinical data management workflows and activities, and lending technical support. Serve as the first line of communication and point of escalation amongst the data abstractors. - Front-line Staff Support: Serve as the primary liaison and initial point of escalation for all abstraction/team-related issues, questions, and concerns. - New Hire Onboarding and Training: Lead training new abstractors and provide ongoing guidance until released into production level activities by supporting the development and implementation of training workflows. - Mentoring and Subject Matter Expertise (SME): Provide ongoing clinical guidance and mentorship to existing abstraction team members, fostering professional development. - Technical Support: - Lead ongoing clinical and medical guidance to engineering, data management specialists, and technical staff to ensure the development of bug-free data for systems such as Electronic Data Capture Systems (ECDs), applications, and workflows. - Apply Medical Coding and Classification skills to support the creation of data standardization and harmonization rules. - Apply Data Management knowledge (e.g., CGDM, GCP) to support establishing logical relationships between clinical events within database schemas. - Apply Technical Skills to translate abstraction and encoding requirements to technical product requirements. - Data Abstraction: Advanced level review, interpretation, abstraction and data encoding. Less than 5% of patients require quality review. - Quality Assurance and Control: Apply and lead efforts in resolving complex or ambiguous data abstraction questions. - Protocol Adherence: Support developing and ensuring strict adherence to all project and research protocols. - Reporting: Track, analyze, and report on key abstraction and staff level metrics. - Process Improvement: Collaborate with cross-functional teams to effectively operationalize new abstraction activities. - Daily Operations: Support direct manager in daily coordination of the clinical data abstraction team's activities and workload distribution. - Conferences and Certifications: Maintenance of all relevant clinical or technical licensures. Attend conferences relevant to role and clinical field. - Other duties and responsibilities to be performed as assigned. Qualifications - Clinical Background: U.S. licensed Nurse, PA-C, NP, or DNP is required with a Master’s degree in health sciences. - Clinical Experience: Minimum of 5-6 years of experience in clinical data abstraction and medical records review, preferably in cancer, women’s health, rare diseases. - Lead Experience: Minimum of 2-3 years of direct experience supervising staff or providing team lead support to senior level management. - Quality and Compliance: Demonstrated commitment to data integrity, quality control processes, and adherence to HIPAA and other data privacy regulations. - Technical Proficiency: Proficient with Microsoft Office Suite or Google Suite, creating pivot tables, creating reports, data analysis, and using systems or databases common in data abstraction, research, or clinical data management. - Certifications/Industry Expertise: CCDM, CCRP, ACR-P, or CRA preferred. - Communication: Excellent written and verbal communication skills, with the ability to effectively collaborate with clinical and non-clinical teams. - Autonomy: Proven ability to work independently, manage time effectively, prioritize and organize tasks, and meet strict productivity and quality deadlines. - General Expertise: - Possess a high level of initiative and self-motivation. - Team player; can navigate supporting highly visible projects and tasks. - In-depth attention to detail and a fast learner. - Responding to shifting priorities and changes. Requirements - Compensation Range: $145,800 — $182,200 USD Benefits - Annual performance incentive bonus - Long-term equity awards - Comprehensive health benefits (medical, dental, vision) - 401(k) with company match - Generous paid time off and company holidays - Additional wellness and work-life benefits

• Design, develop, and execute end-to-end SAP data migration solutions for Finance (FI) and Supply Chain Management (SCM) data objects within a classified Red Network environment, operating under strict data handling, transfer, and validation protocols • Lead data migration workstream activities including legacy data analysis, data mapping, transformation rule development, migration object build, mock migration execution, reconciliation, and cutover planning • Develop and maintain data migration programs using SAP-native migration toolsets including SAP Data Migration Cockpit (LTMC/LTMOM), SAP LSMW (Legacy System Migration Workbench), and custom ABAP migration programs (BDC, BAPI, direct input methods) • Perform detailed analysis of legacy FI and SCM source data including general ledger balances, open items (AR/AP), asset master and values, material master records, inventory balances, open purchase orders, and open sales orders to define extraction, cleansing, and transformation requirements • Design and implement data cleansing, deduplication, and transformation logic to ensure source data meets SAP target structure requirements and federal data quality standards prior to load execution • Define and execute data validation frameworks including pre-load validation rules, post-load reconciliation checks, balance verification, and exception reporting to ensure migration completeness and accuracy • Coordinate data transfer activities across the Red Network boundary in strict compliance with classified data handling procedures, cross-domain solution (CDS) protocols, and agency-approved data movement authorization processes • Collaborate with FI and SCM functional consultants, enterprise architects, and federal IT security personnel to align migration designs with system configuration, authorization concepts, and classified environment constraints • Develop and maintain data migration documentation including migration strategy documents, object specifications, mapping workbooks, test results, reconciliation reports, and cutover runbooks • Support mock migration cycles, dress rehearsal runs, and production cutover execution, providing real-time reconciliation analysis and issue resolution within the constraints of the classified operating environment

• Collaborate with data scientists and machine learning engineers to understand data requirements for LLM and machine learning model fine-tuning. • Design, build, and maintain scalable data pipelines to ingest, process, and store massive and diverse healthcare datasets. • Implement robust data validation and monitoring to ensure the integrity, accuracy, and consistency of all training datasets. • Implement robust data cleaning, validation, and transformation processes to ensure data quality and integrity. • Develop and optimize data structures and schemas for efficient access and utilization by LLMs and machine learning models. • Work with the team to identify and acquire new data sources, ensuring compliance with relevant healthcare regulations (e.g., HIPAA). • Monitor data pipeline performance, troubleshoot issues, and implement optimizations to improve efficiency and reliability. • Document data engineering processes, data models, and data dictionaries. • Stay up-to-date with the latest advancements in data engineering, big data technologies, and machine learning.

• Diseño e implementación de pipelines de datos: Construir y optimizar procesos ETL/ELT en entorno GCP, utilizando BigQuery y herramientas de orquestación, garantizando flujos eficientes, escalables y fiables. • Migración y transformación de datos: Adaptar y trasladar lógica existente desde entornos como Talend/MySQL hacia SQL nativo en BigQuery, mejorando rendimiento y mantenibilidad. • Arquitectura cloud de datos: Diseñar soluciones de procesamiento y almacenamiento en la nube (GCP principalmente, valorable AWS/Azure), asegurando escalabilidad, seguridad y disponibilidad. • Calidad e integridad del dato: Aplicar buenas prácticas de validación, limpieza y monitorización para garantizar datos precisos, consistentes y confiables. • Innovación tecnológica: Explorar e incorporar nuevas herramientas, frameworks y metodologías que optimicen la ingesta, transformación y análisis de datos. • Documentación y buenas prácticas: Documentar pipelines, modelos y procesos para asegurar su comprensión, mantenimiento y reutilización dentro del equipo. • Integración de sistemas: Conectar distintas fuentes de datos (internas y externas) mediante APIs y otros mecanismos, garantizando flujos eficientes e interoperables. • Colaboración con equipos de analítica y BI: Facilitar datos estructurados y de calidad para su explotación en dashboards, análisis y modelos avanzados.