• designing, building, and maintaining dimensional data models that support our analytics, AI, and business intelligence initiatives • working closely with stakeholders to ensure data is accessible, reliable, and optimized for performance • creating and maintaining logical, physical, and conceptual data models in Google Big Query using Dataform • collaborating with stakeholders to understand business needs and deliver data products in support of those needs • developing and managing data dictionaries, metadata, and data lineage • refining and updating models for performance, scalability, and cost efficiencies • ensuring data quality, integrity, and security across all data platforms

• Key contributor for data stewardship workgroups and teams in data management and governance activities • Be a champion or ambassador of metadata and data quality practices across the organization – leading lunch ‘n’ learn sessions, knowledge sharing sessions, etc. • Gain an understanding of the stakeholder’s business culture and priorities, and the need for different modeling techniques when eliciting definitions and data quality requirements • Accountable and responsible for the engineering success of specific data governance engineering programs (e.g. Metadata and Cataloging Program, Data Quality Program) • Perform analysis and research to support and/or develop glossary definitions, policies, programs, standards, guidelines and workflows • Lead the development and maintenance of metadata catalogs • Identify and quantify data related pain points within the organization and assist in the development of remediation plans • Collaborate with SMEs to develop business rules that measure and assure data quality; facilitating discussions with end users and data modelers • Lead efforts to quantify and qualify reported data quality issues; develop and report program metrics to demonstrate progress and compliance • Sets objectives for own job with the understanding of how output would affect and impact other job areas related to own job • Contributes and provides input to the development of operational area goals within the context of own job area • Work consists of tasks that are typically not routine, works independently and applies discretion within established operational boundaries and procedures • Work direction is only provided for new areas of work or unique assignments • Problems and issues faced are vague, and require reasoning and analysis to address problems and issues • Updates and/or modifies work methods based on past experiences, and identifies solutions to problems by recognizing similarities through data trends • Has higher autonomy to arrive at solutions to non-routine problems within established operational boundaries and procedures • Uses personal experiences, makes reasonable judgment calls, and may call on other resources where necessary to identify solutions • Perform any other job duties as requested

• Assisting in the migration of data pipelines from Azure-based infrastructure to GCP • Designing, building, and testing ETL workflows in cloud-native environments • Refactoring and optimizing existing data transformation processes for scalability and performance • Supporting data validation, reconciliation, and quality assurance efforts • Contributing to technical documentation and architectural diagrams • Collaborating with product engineering teams to ensure seamless integration of data services • Participating in code reviews and version control workflows

• Improve the value and utility of provider data and provider data products • Create high-impact data products in the service of our internal business and external provider partners • Collaborate internally to build and maintain provider data assets and documentation • Manage and ensure data quality and assurance for the Provider Analytics team • Collaborate with the Provider Analytics team and Hyphen to build ML-based and provider facing decision support tools • Collaborate with the broader data management and data engineering teams to establish standards and practices around ML Ops