• Own delivery end to end from technical specs, phased migrations, deprecation plans and more. • Take architectural plans and turn them into shipped software- sequenced, risk-managed and on schedule. • Design and implement backend services that handle complex business logic- configuration management, workflow orchestration, data validation and integrations across internal and external systems. • Design APIs, model data, write tests and review pull requests that make the whole team better. • Collaborate on architecture, define domain boundaries, set API standards and bring your own ideas about how to solve hard problems. You will have real influence over how these systems evolve. • Pilot tools where they help, document patterns and scale what works. AI assists; engineers remain accountable for correctness, security and privacy. • Define testing strategies, establish observability standards and strengthen runbooks. • Mentor engineers through code reviews, pairing and technical guidance. • Partner with Product, Partnerships and Clinical Operations to understand requirements and partner-facing implications. • Coordinate with Data/AI on integration points.

• Lead the design and evolution of scalable data architectures, supporting batch, streaming, and AI-driven workloads. • Own end-to-end data pipelines —from ingestion and transformation through to serving analytics and ML/GenAI use cases. • Define and enforce data engineering standards across modelling, orchestration, observability, and reliability. • Mentor and guide data engineers through code reviews, design discussions, and architectural decisions. • Translate business problems into scalable technical solutions, balancing speed, quality, and long-term maintainability. • Drive the use of agent-based solutions across the development lifecycle, designing autonomous and semi-autonomous workflows that deliver measurable business value. • Clearly document architectures and workflows to support shared understanding and operational excellence. • Build and optimize data pipelines using Databricks, Spark (PySpark), Snowflake, Apache Airflow, and Terraform. • Design performant data models and lakehouse structures (Delta, Unity Catalog) for analytics and downstream AI consumption. • Leverage AWS-native services (e.g. S3, EMR, DynamoDB) to deliver cost-efficient, production-grade solutions. • Implement robust data quality, testing, and monitoring (e.g. Great Expectations, logging, alerting). • Design data pipelines that power Generative AI applications, including data preparation, enrichment, and feature generation. • Integrate 3rd party APIs into data workflows for use cases such as: Automated data enrichment and classification Intelligent summarization and insight generation Metadata generation and semantic search enablement AI-assisted reporting and decision support. • Collaborate with ML and Product teams on prompt design, evaluation, and governance, ensuring responsible and reliable AI usage.

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.