• Design and implement scalable, reliable, and efficient data pipelines to support clinical, operational, and business needs. • Develop and maintain architecture standards, reusable frameworks, and best practices across data engineering workflows. • Build automated systems for data ingestion, transformation, and orchestration leveraging cloud-native and open-source tools. • Optimize data storage and processing in data lakes and cloud data warehouses (Azure, Databricks). • Develop and monitor batch and streaming data processes to ensure data accuracy, consistency, and timeliness. • Maintain documentation and lineage tracking across datasets and pipelines to support transparency and governance. • Work cross-functionally with analysts, data scientists, software engineers, and business stakeholders to understand data requirements and deliver fit-for-purpose data solutions. • Review and refine work completed by other team members, ensuring quality and performance standards are met. • Provide technical mentorship to junior team members and collaborate with contractors and third-party vendors to extend engineering capacity. • Use Databricks, DBT, Azure Data Factory, and SQL to architect and deploy robust data engineering solutions. • Integrate APIs, structured/unstructured data sources, and third-party systems into centralized data platforms. • Evaluate and implement new technologies to enhance the scalability, observability, and automation of data operations. • Proactively suggest improvements to infrastructure, processes, and automation to improve system efficiency, reduce costs, and enhance performance.