Role Description The ACR seeks a Team Lead to provide Data Management leadership to the ACR study initiatives across the College. - Provides vision, leadership, and mentoring to a team of Data Management professionals; - Oversees and leads review of protocols and coordinates study start-up and active study management activities for trials; - Identifies and develops case report forms, program edit checks, and web logic specifications; - Drives innovation by integrating AI/ML capabilities into standard quality control processes; - Manages research protocols to include performing case-level reviews, queries, quality control review checks, and database lock activities. Qualifications - 8+ years of related experience preferred; - Bachelor’s degree in Life Science or BS degree or professional nursing degree/other healthcare certification required; - Supervisory experience preferred; - Experience developing EDC systems for clinical trials a plus; - Experience in a multi-modality clinical trials group or research team a plus; - Certified Medidata Rave® Study Builder, C#, and SAS programming experience a plus. Benefits - Competitive compensation and equitable pay; - Defined contribution pension plan; - 403(b) plan; - Generous paid time off package; - Insurance plans with leading providers; - Flexible spending; - Tuition reimbursement; - Training opportunities; - Wellness reimbursement. Company Description The American College of Radiology is a professional association serving over 40,000 members nationwide. The ACR’s Center for Research and Innovation (CRI) has been a world leader in radiology clinical research for over 50 years, conducting clinical research trials and other projects that have transformed the practice of radiology.

Role Description - Experience: 8–10 years in Data Engineering and Data Analysis. - Informatica Expertise: Strong hands-on experience in Informatica PowerCenter/IDQ for ETL design, development, and optimization. - PySpark Development: Advanced skills in PySpark for large-scale data processing, transformation, and analytics. - Hadoop Ecosystem: Solid working knowledge of Hadoop technologies (HDFS, Hive, Sqoop, MapReduce). - Programming Skills: Proficiency in Python and Kafka for streaming and batch data pipelines. - Database & Modeling: Strong understanding of database concepts, data design, data modeling, and ETL workflows. - ETL Lifecycle: Experience in analyzing, designing, and coding ETL programs including data extraction, ingestion, quality checks, normalization, and loading. - Agile Delivery: Hands-on experience with Agile methodology and Jira for project delivery. - Client Interaction: Proven ability in client-facing roles with strong communication and leadership skills to coordinate across SDLC. Qualifications - Master’s or Bachelor’s degree in Computer Science or related field. - Strong problem-solving skills and ability to work in cross-functional teams. Requirements - Preferred Skills: - Exposure to AWS data components and analytics. - Familiarity with machine learning models and AI concepts. - Experience with data modeling tools such as Erwin.

• Develop, optimize, and maintain reliable data pipelines and processing components using Google Cloud Platform (GCS, BigQuery, Looker) • Integrate and manage data from various sources, including marketing platforms and ad networks • Design and maintain scalable data models and datasets that support analytics and business decision-making • Translate business requirements into efficient, scalable data solutions • Improve data accessibility, reliability, and performance across the data ecosystem • Build tools and frameworks that enable faster, more effective analysis and self-service reporting • Ensure data quality, consistency, and governance throughout the data lifecycle • Optimize data infrastructure and processing costs while maintaining scalability and performance

• The Senior Data Engineer will own the data engineering function on K2Share's Federal Team, partnering with technical and product leadership to deliver data products that support mission-critical decision-making for federal agency clients. • Design and build relational data layers that handle OSCAL and other structured compliance data - including ingestion, validation, transformation, and export workflows that preserve fidelity to source schemas across the full data lifecycle • Design and maintain data models that support governance, risk, compliance, scoring, and reporting workflows for federal cybersecurity programs, with OSCAL as the connective layer across them - including long-term retention and archival policies that align with federal recordkeeping and audit requirements • Design and build big-data processing pipelines on Databricks (PySpark, Delta Lake, Unity Catalog) that normalize cybersecurity data from across federal agency environments and produce analytical layers for trend analysis, executive reporting, and cross-program insights • Optimize data systems for performance and cost - identifying I/O and compute bottlenecks, scaling compute responsibly, and balancing throughput against the cost discipline federal engagements require • Architect, build and maintain AWS data infrastructure that meets federal security and operational requirements - working across services such as S3, Bedrock, Lambda, Fargate, and EC2 in support of compliance and analytical workloads • Design and implement audit-ready data primitives - change capture, access controls, validation, and lineage — that support agency reporting and continuous monitoring needs • Lead AI-first development and responsible AI deployment on the data team - using AI development tools as a standard part of the engineering loop, prototyping AI-assisted compliance workflows, and designing the production AI systems behind them (RAG architectures, vector store management, conversational agents, prompt and output guardrails, and evaluation pipelines), in alignment with federal AI governance guidance (OMB, NIST AI RMF) • Engage with federal agency stakeholders and internal teams during requirement discovery, delivery, and ongoing support — translating compliance needs into data products and customer feedback into improvements