• Design, build, and maintain scalable, efficient data pipelines for ETL/ELT processes on AWS. • Develop, test, and deploy robust solutions using SQL and Python for data transformation and analysis. • Implement and manage data warehousing solutions using Redshift Serverless and other AWS data services. • Leverage dbt (Data Build Tool) for data modeling, transformation, and documentation. • Utilize workflow orchestration tools such as Temporal for pipeline automation. • Work with healthcare quality metrics for value-based care and ensure data alignment with industry standards. • Collaborate with stakeholders to integrate population health tools and analytics into data workflows. • Develop and maintain familiarity with FHIR data models and healthcare interoperability standards. • Ensure compliance with HIPAA and other healthcare regulatory requirements in all data handling processes. • Identify and resolve performance bottlenecks in data pipelines, ensuring high availability and reliability. • Optimize data storage and querying performance within Redshift Serverless and AWS infrastructure. • Stay current with emerging trends in data engineering and healthcare technology. • Partner with data and engineering teams to ensure data is accessible and meets business requirements. • Develop scalable solutions for integrating complex healthcare datasets, ensuring data quality and accuracy. • Contribute to the design and implementation of secure, scalable, and efficient data architecture on AWS.

• Provide technical leadership, analytical expertise, and program oversight in data engineering • Collaborate with software developers, data scientists, and product managers to understand requirements • Contribute technical expertise in data engineering to design and implement data solutions • Participate in planning sessions to develop data pipeline architecture • Design data ingestion, transformation, and storage workflows that are scalable • Implement data quality checks and validation procedures • Conduct comprehensive testing, including unit, integration, and system testing • Document data engineering processes, pipeline configurations, and data flows • Foster effective collaboration with cross-functional teams • Communicate complex technical information clearly and effectively • Manage projects, including planning, execution, and delivery

• Contribute to enterprise data transformation initiatives • Design and implement data ingestion pipelines • Monitor and optimize data ingestion and processing workflows • Collaborate with cross-functional teams • Support continuous improvement of the data platform

• Engage in a large-scale data transformation initiative • Analyze and modernize complex data transformation logic • Architect and implement end-to-end data ingestion frameworks • Define and analyze performance metrics • Perform advanced performance tuning for Databricks operations • Provide technical leadership and mentorship