Data Engineer

• Design, build, and maintain scalable data pipelines to support analytics, operations, and reporting.
• Develop and manage data models, schemas, and warehouse structures (e.g., in BigQuery) to ensure data reliability and performance.
• Collaborate with analytics and engineering teams to implement efficient ETL/ELT processes that align with business and clinical workflows.
• Continuously optimize data architecture for scalability, cost-efficiency, and ease of use.
• Partner with analytics, product, and engineering teams to understand data needs and translate business requirements into technical solutions.
• Support clinical, operations, finance, and growth teams by ensuring the availability and accuracy of high-quality data.
• Serve as a bridge between technical and non-technical stakeholders, ensuring alignment on data definitions, availability, and performance.
• Build and manage data workflows using modern tools (e.g., Airflow, dbt, Fivetran, Looker, or equivalent).
• Ensure all data systems and processes comply with HIPAA and healthcare data security requirements.
• Evaluate emerging technologies and best practices to improve pipeline performance, reliability, and observability.
• Implement robust data validation and monitoring systems to ensure data accuracy and completeness.
• Maintain clear documentation, version control, and consistent naming conventions as part of broader data governance efforts.
• Collaborate with analytics leadership to define and enforce data standards, including lineage tracking and source-of-truth definitions.
• Proactively identify opportunities to automate manual processes and improve data delivery speed.
• Stay current with trends in data engineering, cloud infrastructure, and healthcare data management.
• Contribute to a culture of learning and collaboration within the data and analytics organization.

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Data Engineer