• To design, development, and enhancement of our AWS data environment.

• Participate in cross-functional meetings to define project objectives and scope. • Analyze and prioritize business requirements related to data. • Design and develop data extraction, transformation, and loading pipelines (ETL/ELT). • Develop applications for extracting data from various sources and formats (JSON, XML, PDF, etc.). • Write clean, efficient, and scalable code, adapting to new technologies as needed. • Implement data quality methodologies throughout ingestion and transformation processes. • Optimize services, queries, and processes to reduce operational costs and improve performance. • Validate the functionality, integrity, and security of data engineering processes.

• Design, develop, and maintain scalable and efficient data pipelines (ETL/ELT). • Build and optimize modern data architectures (Data Warehouse and Lakehouse). • Implement data models (primarily dimensional modeling using the Kimball approach). • Integrate multiple data sources ensuring quality, consistency, and availability. • Perform data profiling and anomaly detection. • Optimize SQL queries and performance across data platforms. • Collaborate on defining standards, best practices, and data architecture. • Implement CI/CD processes and DevOps practices applied to data pipelines. • Work under Agile methodologies (Scrum/Kanban) in collaboration with multidisciplinary teams.

• Serve as a technical expert for Snowflake and Azure , leading hands ‑ on implementation across ingestion, transformation, orchestration, and platform optimization. • Develop and maintain API ‑ driven integrations using MuleSoft , enabling secure, scalable connectivity between cloud and on ‑ prem systems. • Drive the migration from on ‑ prem SQL Server environments to Snowflake , including re ‑ architecting pipelines, data models, and workflows for the cloud. • Implement engineering best practices, including CI/CD for data, infrastructure ‑ as ‑ code, version control, testing frameworks, and observability. • Independently troubleshoot complex data and platform issues , identifying root causes and designing effective long ‑ term solutions. • Collaborate with data architects, analysts, and business partners to translate requirements into robust, production ‑ ready solutions. • Mentor and support data engineers , providing technical guidance, code reviews, and thought leadership to elevate engineering standards. • Optimize data models, storage patterns, and compute usage to ensure cost ‑ efficient and high ‑ performance operations. • Partner with IT, security, and governance teams to ensure compliance, data quality, and secure handling of sensitive healthcare data. • Evaluate and introduce new tools, frameworks, and cloud capabilities that enhance our data platform and engineering productivity.