• Design, build, and maintain scalable data pipelines and architectures to support analytics, machine learning, and operational applications. • Collaborate with cross-functional teams to translate complex operational needs into reliable, well-modeled datasets. • Integrate and normalize data from multiple structured and unstructured healthcare sources (EHRs, scheduling systems, operational databases, etc.). • Optimize query performance and data processing for speed, scalability, and cost efficiency. • Implement best practices for data quality, governance, and security in compliance with healthcare regulations (e.g., HIPAA). • Support deployment, monitoring, and troubleshooting of production data systems.

• Spearhead the discovery, evaluation, and integration of new datasets • Facilitate the technical management of data assets • Translate product / analytical vision into highly functional data pipelines • Set the standard for data engineering practices within the company

• Assist in the integration and maintenance of existing and new datasets, working closely with stakeholders to ensure data documentation and modeling are current and impactful. • Execute the technical management of data assets by monitoring pipeline health, tracking data context through the dataset lifecycle, and sustaining partnerships with ingestion teams. • Develop and refine data transformation pipelines based on established architectural patterns to support high-quality, trusted data products. • Adhere to established data engineering practices and architectural standards, contributing to a culture of reliable and continuous improvement within the team.

• Be part of a team responsible for supporting new and existing customers in their data engineering needs. • Guide customers to make the best technical decisions to achieve their goals. • Actively work across multiple customer accounts which you would need to track and report on their progress. • Design, build and operationalize complex data solutions, fix issues, apply transformations, and recommend data cleansing / quality solutions. • Analyze data sources to determine their value and recommend them to include in analytical processes. • Incorporate core data management competencies including data governance, data security and data quality. • Collaborate within and across teams to support delivery and educate end users on data products / analytic environments. • Perform data and system analysis, assessment and resolution for defects and incidents of moderate complexity and correct as appropriate. • Test data movement, transformation code, and data components.