The Senior Clinical Data Research Engineer will be an individual contributor in the Medical Affairs department. The role will encompass data analysis and curation from multiple large clinical datasets to inform internal reports, external presentations, manuscripts, and publications, as well as to guide future clinical trial design. This is a remote role. Interpret clinical case reports and develop an understanding of clinical science of immune-mediated conditions, including sepsis, to inform study design and content of case report forms. Support the design, interpretation, reporting, and publication of clinical studies, including detailed participation in clinical endpoint design and process, supporting EDC builds, and study execution. Perform data analysis and develop data-driven models to track disease and outcome trends, assess value propositions, and evaluate assay clinical utility. Support quality improvement activities for customers by building systems and tools for post-implementation data analysis. Utilize data to track the performance and effectiveness of the IntelliSep solution in improving clinical outcomes, operational efficiency, and financial performance, and provide insights into customer-related metrics and the potential impact on patient outcomes and hospital reimbursement. Collaborate with cross-functional teams to gather data and gain insights into current-state workflows and performance related to sepsis management and clinical workflows within the emergency department. Appropriately apply visualization best practices and data storytelling techniques and deliver a clear and concise presentation of findings tailored to the audience. Develop documentation and methodologies for analyses and deliverables. Develop statistical models using clinical and biological data to inform clinical trial design. Write statistical analysis plans, including statistical methodology and programming procedure. Contribute analysis and graphs to educational and marketing materials, company reports, and scientific publications.

• Design and administer data systems: Implement cloud-native data solutions across cloud services such as AWS Glue, AWS Lambda, AWS Step Functions, Redshift, Aurora, Amazon S3, MWAA-Airflow. • Design and implement scalable data architectures using AWS cloud services, including data lakes, data warehouses, bulk data ingestion, and transaction processing. • Collaborate with customers and teammates to comprehend data requirements and translate business needs into well-designed data models. • Assemble large, complex data sets that meet functional business requirements. • Stay current with AWS services and recommend suitable tools for specific data engineering tasks. • Develop and support the internal applications using Python, SQL, and Stored Procedures. • Build reliable data processes: Develop, implement, and maintain ETL processes to ingest, clean, and transform complex health care data into our application data platforms. • Automate manual processes and to optimize data delivery and reduce operational friction. • Optimize data pipeline performance: Monitor and tune data pipelines and data storage for performance, cost, and reliability. • Implement caching mechanisms and data partitioning strategies to enhance query efficiency and reduce data processing times. • Lead and collaborate company-wide: Provide technical guidance and mentorship within the data engineering team and foster a collaborative and innovative environment. • Partner closely with product and analytics team to understand business goals and engineer solutions to the right problems. • Protect and govern data responsibly: Implement and enforce data security measures and maintain data governance standards and access controls to protect sensitive data. • Ensure compliance with health care data regulations and industry best practices.

• Architect and implement the unified GTM data spine across CRM, marketing automation, product telemetry, billing, enrichment providers, partner data, and the warehouse. • Establish identity resolution, deduplication, and source-of-truth logic across systems. • Define canonical schemas that we’ll use to operate the business, enable AI across teams and drive results. • Build scalable ELT/ETL pipelines and orchestration workflows tailored to revenue activation. • Implement governance, lineage, access control, and quality monitoring for GTM data assets. • Partner closely with the BI / enterprise data team to align on shared infrastructure while owning GTM-specific models and activation layers. • Develop and productionize segmentation and scoring models (ICP scoring, ABM prioritization, expansion propensity, trial health, pipeline likelihood). • Apply statistical and machine learning techniques to improve scoring, targeting, routing, and revenue predictability. • Design experimentation and measurement frameworks for GTM programs. • Operationalize predictive outputs directly into GTM systems via agents, reverse ETL, or custom integration (Salesforce workflows, outbound automation, lifecycle programs, ABM platforms, partner programs). • Architect customer and prospect intelligence systems integrating first-party, enrichment, and ecosystem signals. • Develop frameworks for ecosystem intelligence (partner influence, marketplace signals, derived demand). • Enable real-time or near-real-time signal activation to sales and marketing teams. • Design structured data systems that power future AI-driven revenue workflows. • Manage and direct external agency/consulting partners executing against the GTM data roadmap. • Establish architectural standards and technical review processes. • Define the build vs. buy strategy for GTM data systems. • Develop the long-term roadmap for in-house data capability.

• Data Pipeline Engineering: Architect, build, and maintain complex, real-time, and batch data pipelines using Azure Data Factory, Python/PySpark, and Databricks. • Architecture & Modelling: Design and implement modern data warehouse solutions, data models, and data lakes, optimizing for performance and scalability. • Data Ingestion & Integration: Ingest, cleanse, and transform data from diverse sources into usable data structures for analytics. • Security & Governance: Implement security features, including role-based access control (RBAC), data encryption, and governance via Azure Purview.