• Build and maintain data infrastructure that enables the collection, storage, and retrieval of data; • Create new data flows by integrating our data sources and ensuring they are reliable and efficient; • Develop ETL pipelines, data warehousing, and data modeling to support business needs; • Ensure data quality monitoring, reliability, and lineage by developing processes and tools to identify and correct data quality issues; • Collaborate with other members of the Data & Analytics Team to optimize the data infrastructure and improve data governance; • Provide documentation and training to end-users on data sources, pipelines, and data quality procedures; • Stay current with the latest technologies and techniques related to data engineering, and identify opportunities to improve data infrastructure and analysis.

• Design, develop, and maintain scalable data pipelines for processing and analyzing large volumes of data • Develop and optimize data workflows using Databricks, leveraging Spark for large-scale data processing • Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and ensure data integrity and quality • Utilize your expertise in Python for scripting and coding tasks related to data processing and analysis • Understand and implement business rules in Python for data transformation • Implement ETL processes to integrate data from various sources into data warehouse or data lake solutions • Optimize big data storage and processing • Troubleshoot and resolve data-related issues, ensuring the reliability and performance of our data infrastructure • Follow emerging trends and technologies in the data engineering space and make recommendations for continuous improvement • Optimize and tune data workflows for maximum efficiency and scalability. • Implement data security best practices to protect sensitive information and ensure compliance with data protection regulations. • Develop and maintain API integrations to facilitate seamless data exchange between systems and applications

• Design, build, and maintain scalable data pipelines and architectures to support analytics, machine learning, and operational applications. • Collaborate with cross-functional teams to translate complex operational needs into reliable, well-modeled datasets. • Integrate and normalize data from multiple structured and unstructured healthcare sources (EHRs, scheduling systems, operational databases, etc.). • Optimize query performance and data processing for speed, scalability, and cost efficiency. • Implement best practices for data quality, governance, and security in compliance with healthcare regulations (e.g., HIPAA). • Support deployment, monitoring, and troubleshooting of production data systems.

• Spearhead the discovery, evaluation, and integration of new datasets • Facilitate the technical management of data assets • Translate product / analytical vision into highly functional data pipelines • Set the standard for data engineering practices within the company