• Thoroughly understand all Underwriting and Rebate Administration data sources, fields, and relationships, including historical claims, Medi-Span tables, NCPDP tables, and Capital Rx’s Book of Business data to interpret trends, identify patterns, and conduct complex data analyses in support of Underwriting, Rebate Administration, and Sales business goals • Investigate new data sources for the purpose of building & maintaining data pipelines to clean, transform, and aggregate disparate data • Use agile software development to create, maintain, and improve back-end systems for data extracts, pricing processes, and analytic tools to advance the department’s technical capabilities • Model front-end views and back-end data sources to draw a comprehensive picture of the user experience and analytic pipelines throughout the system and to enable powerful data analysis • Troubleshoot and customize infrastructure code in SQL, R, and Python to diagnose and solve data or process issues • Work closely with our Underwriting and Clinical teams to help build complex algorithms that provide useful insights into our data • Assist in the analysis and development of new models and front-end tools that can be used to make predictions and answer questions for financial modeling & reporting • Collaborate with various teams across the company on data sources and analytic methodology in support of Underwriting, Rebate Administration, and company objectives • Provide standard and ad-hoc analytics to support the Underwriting and Rebate Administration teams for the successful completion of bid opportunities and rebate payment management

• Lead the design, develop and implement data models for enterprise-level applications and systems • Create short-term tactical solutions to achieve long-term objectives and an overall data management roadmap • Propose and collaborate on plans for security, backup, disaster recovery, business continuity, and archiving • Oversee the development team by monitoring and reviewing the quality, performance, security, and scalability of solutions

• Design, implement, and maintain data pipelines and ETL processes supporting ingestion, transformation, and validation of mission data • Develop and optimize data models and schemas across relational and non-relational databases to support system integrations and analytics • Collaborate with system architects, integration developers, and data analysts to ensure data consistency, security, and integrity across cloud environments • Implement data migration and synchronization between legacy systems, applications, and modern cloud platforms • Utilize AWS services (Glue, Lambda, S3, RDS, Redshift, Kinesis) to build and sustain scalable and fault-tolerant data infrastructure • Support data validation and reconciliation, performing quality checks and developing reports to ensure accuracy • Integrate data from APIs, streaming sources, and file-based systems into centralized repositories or data lakes • Automate data workflows using infrastructure-as-code and CI/CD principles to ensure repeatability and efficiency • Monitor and troubleshoot data pipeline performance, ensuring adherence to SLAs and operational reliability • Implement data encryption, masking, and access controls in compliance with DoD cybersecurity policies and RMF requirements • Support development of dashboards and analytics products, enabling data-driven insights for mission stakeholders • Maintain documentation and metadata repositories, including data dictionaries, lineage, and technical specifications • Participate in Agile sprints, contributing to backlog refinement, testing, and cross-functional collaboration

• Design, build, and maintain **schemas and data models** • Optimize table layout, partitioning, indexing, and compression for high-volume data • Ensure fast, efficient querying for logs, requests, metrics, and performance traces • Maintain ingestion pipelines for billions of records • Build robust pipelines for: - API logs - Model inference logs - Error events - Usage & integration events - GPU & system metrics • Implement ETL/ELT workflows to transform raw data into analytics-ready structures • Ensure quality, reliability, and real-time availability of data sources • Build tooling to support large-scale **log analysis** • Enable deep investigation into latency, throughput, errors, and bottlenecks • Provide the raw data foundation for E2E inference-time monitoring • Help debug production issues using logs and traces • Work closely with DevOps, ML, and backend engineering • Integrate pipelines with monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry) • Automate ingestion and cleanup tasks • Build internal libraries or utilities to support monitoring and debugging workflows • Provide clean data interfaces for the Data Expert (dashboards, monitoring, analytics) • Support engineering teams by exposing the right logs and metrics • Contribute to debugging, RCA (root cause analysis), and performance optimization initiatives