• Evaluate and restructure existing data architecture to support scalability and performance • Design new schemas, relationships, and data models that align with business logic and analytics needs • Build and maintain a HelpGrid-centric data layer that consolidates fragmented sources into a central structure • Provide strategic guidance on how data should be organized, named, and modeled for long-term sustainability • Establish best practices for schema versioning, documentation, and change control • Design and implement the company’s first ETL framework, defining how data is extracted, transformed, and loaded from multiple sources • Build automated, reliable pipelines that move data from the centralized database and external tools into analytics-ready structures • Standardize transformation logic to clean, normalize, and enrich data for business use • Implement pipeline monitoring, error handling, and validation for data quality assurance • Provide architectural and workflow recommendations for how data should flow between systems and teams • Define how analysts should access, refresh, and use data safely and consistently • Partner with the Data & Analytics Manager to align the engineering roadmap with BI and reporting priorities • Develop scalable, reusable scripts and frameworks that simplify ongoing data management • Integrate data from internal and third-party platforms into a centralized environment • Optimize query and pipeline performance for high-volume operations • Build APIs or microservices for data synchronization and access • Document data lineage, schema definitions, and system dependencies • Implement data access controls, validation checks, and compliance standards • Maintain transparent documentation for analysts, developers, and leadership • Promote data stewardship and governance best practices across departments

• Build and maintain financial and funds-flow pipelines in Databricks • Bridge Snowflake and Databricks to support finance reporting and reconciliation • Partner with finance stakeholders to validate logic and outputs • Ensure auditability, accuracy, and reliability of financial data • Contribute to platform standards and best practices

• Design and build Databricks pipelines for provider, patient, and enterprise data • Support migration of existing Health Catalyst workloads • Bridge Snowflake and Databricks environments • Work closely with clinical, operational, and analytics stakeholders • Ensure data quality, lineage, and performance • Participate in backlog prioritization and execution

• Rapidly design and implement Databricks data pipelines (Spark / PySpark) for marketing use cases • Bridge Snowflake and Databricks environments to enable seamless data movement and analytics • Ingest and model marketing data (CRM, campaigns, digital, attribution, engagement) • Support the handoff of in-flight work from Health Catalyst • Partner directly with marketing stakeholders to translate needs into execution • Stabilize and secure the Databricks platform to support downstream analytics • Ensure data quality, performance, and documentation standards