• Architect, implement, and maintain data ingestion and transformation pipelines using modern workflow orchestration tools (e.g. Dagster). • Identify, catalog, and integrate internal and external data sources used across research efforts. • Operationalize bioinformatics pipelines that support large-scale batch processing, incremental updates, and backfills within AWS. • Normalize and structure heterogeneous data into consistent, reusable representations that support downstream analysis, modeling, and querying. • Populate and maintain patient-centric data models in shared storage systems (e.g., graph and relational databases). • Collaborate with backend and AI engineers to design data-access patterns that support analytics applications and AI-driven interactions. • Contribute to backend services and APIs that expose integrated data to internal tools and applications. • Participate in the evolution of AI-enabled analysis workflows, including tooling that supports LLM- or agent-based interactions with data. • Contribute to system-level design decisions around data flow, service boundaries, reliability, and scalability. • Write clean, tested, and well-documented Python code that meets production software engineering standards. • Debug and resolve complex data quality, pipeline, backend, and infrastructure issues in a distributed environment.

• Enterprise Data Strategy & MDM Design • MDM Architecture: Design the strategy and logical architecture for linking "Core Entities" including but not limited to Providers, Practices, Patients across the enterprise ecosystem to enable a holistic view of the business and to eliminate redundant manual workflows. • Business Data Warehouse Design: Architect the centralized data hub within Snowflake, ensuring it is structured to support complex cross-functional reporting on revenue and performance. • Automation Roadmap: Partner with the Integrations team to define how MuleSoft should be leveraged to automate data flows between systems (e.g., ensuring a signed contract in Ironclad triggers the appropriate data synchronization across Salesforce and financial systems). • Lifecycle Mapping: Create end-to-end data flow diagrams that capture the full customer journey, identifying "golden record" sources for every data point. • Cross-Functional Governance: Lead the effort to document and standardize business definitions of data. You will serve as the bridge between technical teams and stakeholders in Product, Security, Compliance, Growth, Provider Networks, and Finance. • Data Cataloging: Leverage DataHub to build and maintain a comprehensive data catalog, ensuring technical and non-technical users understand data lineage, definitions, and ownership. • Governance Framework: Establish the standards for data quality, security, and privacy in collaboration with the Security and Compliance (CRG) teams. • Insights Readiness: Ensure the data architecture provides the BI team with clean, reliable, and well-documented datasets to drive analysis on business performance and success metrics. • Technical Consulting: Act as the internal consultant for business units looking to integrate new data types or systems into the enterprise landscape. • Organizational Scaling: Define the long-term vision for the Data Architecture function, including the future hiring plan and organizational structure as the complexity of our data ecosystem grows. • Mentorship: Provide high-level technical guidance to engineers and systems analysts across the Enterprise Applications team.

• Work and collaborate closely with BI, Product Engineering, and cross-functional stakeholders to gather requirements, define data models, and deliver actionable data products. • Architect and manage OLAP data platform (Redshift and related components) and partner closely with Product Engineers on OLTP data DevOps to ensure smooth cross-system integrations and data flows. • Design, build, and maintain scalable ETL/ELT pipelines across batch and real-time environments. • Build and optimize reliable, observable, and maintainable pipelines using AWS Glue, Kafka/Kinesis, and Python. • Own and evolve data models that support analytics, product usage tracking, and real-time decision-making. • Develop and enforce best practices for automated testing, data validation, quality checks, and CI/CD workflows for data systems. • Improve data reliability, governance, lineage, and observability across the stack. • Mentor other engineers and help set strong engineering and data platform standards.

• Lead and manage the data engineering team, providing technical direction, mentorship, and performance oversight. • Design, develop, and optimize enterprise data architectures and pipelines to support business intelligence, analytics, and downstream applications. • Collaborate with cross-functional stakeholders to gather requirements, prioritize initiatives, and deliver data solutions aligned with business needs. • Establish, implement, and enforce data governance, data quality, and data security standards across the organization. • Partner with IT and infrastructure teams to support cloud-based data platforms, ensuring system performance, scalability, and reliability. • Oversee project planning, execution, and delivery for data engineering initiatives, utilizing established project management practices and tools. • Maintain comprehensive documentation for data architectures, pipelines, processes, and standards. • Monitor industry trends and emerging technologies, including AI-driven data solutions, and recommend innovative approaches to advance data engineering capabilities. • Ensure best practices are followed for handling sensitive data and meeting regulatory and compliance requirements within financial services.