Staff Engineer, Data Modeler

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Job Description: The Staff Engineer, Data Modeler will be responsible for shaping and managing the data models and architecture, enabling the organization to store, analyze, and leverage large-scale healthcare data efficiently. This includes developing and implementing reliable, scalable, and effective data models for various data warehouse solutions using cutting-edge tools such as Fivetran, DBT, Snowflake, AWS, Atlan, Erwin ER Diagrams, and Sigma Computing. This role will collaborate with a diverse set of stakeholders to develop a comprehensive data architecture that supports decision-making, reporting, analytics, and data governance. This role requires significant experience with dimensional models, RDBMS, cloud platforms, and ETL processes. This role will be responsible for defining and designing data models that support data governance, data quality, and master data management (MDM), while also working with stakeholders to implement data-driven solutions that enhance business outcomes in the healthcare sector. A strong focus will be placed on creating a trusted data environment by ensuring accurate data mapping, implementing Golden Record practices. Data Modeling & Architecture: Design and implement conceptual, logical, and physical data models, including Entity-Relationship (ER) models, Star Schema, Snowflake Schema, Data Vault Modeling, and Dimensional Modeling. Lead the design of normalized and denormalized structures to meet business requirements and ensure optimal performance of the Data Warehouse and Data Marts. Collaborate with business and technical teams to map business requirements to data models, ensuring that Master Data Management (MDM) processes and Golden Record concepts are well-defined. Build and maintain a comprehensive Business Glossary and Data Dictionary to standardize definitions and ensure consistency across the organization. Data Lineage & Mapping: Ensure that data lineage is accurately defined, visualized, and documented across the Data Warehouse environment. Oversee the data mapping process to track the flow of data from source to destination, ensuring consistency, integrity, and transparency of data throughout its lifecycle. Data Governance & Quality: Implement Data Governance processes to manage data access, quality, security, and compliance. Define and enforce Data Quality standards and practices, including Data Cleansing, to ensure data integrity and accuracy within the data warehouse environment. Work with stakeholders to establish governance frameworks for Data Lineage, ensuring data traceability, and transparency across the platform. Work with data architects and IT leadership to establish guidelines for data access, data security, and lifecycle management. Real-Time Data Ingestion & Change Data Capture (CDC): Design and implement real-time data ingestion pipelines using Kafka, AWS Kinesis, or Snowpipe to enable streaming data integration into the data warehouse. Implement Change Data Capture (CDC) mechanisms to efficiently capture and propagate data changes from operational systems using tools such as Fivetran or AWS Lambda. Ensure low-latency processing, incremental updates, and data availability for real-time analytics and reporting. Quality Assurance & Continuous Improvement: Ensure high standards for data quality through rigorous testing, data validation, and performance optimization. Continuously evaluate and improve data modeling processes, tools, and methodologies. Automation & Process Improvement: Work with Data Engineers and development teams to improve data platform automation and enhance the data modeling lifecycle. Continuously monitor, test, and optimize data models and pipelines to ensure scalability, flexibility, and performance of the Data Warehouse. Documentation & Reporting: Maintain clear and up-to-date documentation for data models, data lineage, data mappings, and architectural decisions. Create and present technical diagrams, such as Entity-Relationship Diagrams (ERDs), to stakeholders and ensure alignment with business objectives. Platform Design & Deployment: Develop data architecture for the analytics platform on Snowflake and integrate with other AWS tools for robust data management. Work closely with data engineers to automate data pipeline deployments and updates using Fivetran, DBT, and cloud-based solutions. Stakeholder Collaboration: Partner with Product Manager, and other technical teams to define requirements and deliver optimal data architecture solutions. Conduct regular meetings to communicate technical decisions and ensure alignment with business goals and strategy. Contribute to proposal creation and RFP submissions, ensuring technical feasibility and best practices. Documentation & Reporting: Document all design decisions and data models, adhering to existing guidelines and ensuring clear communication across teams. Create presentations and visual data architecture diagrams for internal and external stakeholders. Perfor