• Design, develop, and maintain scalable data pipelines using modern distributed data processing platforms and cloud environments. • Build and optimize ETL/ELT processes following industry best practices and cloud-native architectures. • Implement data models aligned with modern Data Lakehouse principles and data architecture frameworks. • Ensure data quality, consistency, and performance across ingestion, staging, and curated data layers. • Collaborate with data architects, analysts, and business stakeholders to understand complex healthcare data requirements. • Develop reusable data transformation logic and modular processing components for efficient, maintainable systems. • Support deployment processes following CI/CD and DevOps best practices. • Monitor and optimize data workflows for performance, scalability, and reliability in production environments. • Contribute to data governance, security, and compliance practices relevant to regulated healthcare environments.

• Design, develop, and maintain scalable data pipelines and ETL processes. • Build and optimize data solutions within Azure cloud environments, including Azure Synapse and Azure Data Factory. • Integrate data from multiple sources through APIs and other system integration methods. • Develop and support data analytics and reporting solutions using Power BI. • Ensure data quality, integrity, security, and performance across data platforms. • Collaborate with stakeholders to gather requirements and deliver data-driven solutions supporting healthcare and VA initiatives. • Manage and track development activities using Agile methodologies and tools such as Jira. • Take on additional tasks and responsibilities as needed to support team objectives and ensure the success of the project.

• Help build, maintain, and scale our data pipelines that bring together data from various internal and external systems into our data warehouse. • Partner with internal stakeholders to understand analysis needs and consumption patterns. • Partner with upstream engineering teams to enhance data logging patterns and best practices. • Participate in architectural decisions and help us plan for the company’s data needs as we scale. • Adopt and evangelize data engineering best practices for data processing, modeling, and lake/warehouse development. • Advise engineers and other cross-functional partners on how to most efficiently use our data tools.

• Orchestrate High-Velocity Workflows: Leverage advanced agentic coding tools (e.g., Cursor, multi-agent environments) to dramatically accelerate feature prototyping, code generation, and test coverage. • Own the Guardrails & Quality: Act as the ultimate reviewer and architect; define the specifications, establish repo-context guardrails, and review AI-accelerated output for hidden security risks, scale bottlenecks, and architectural alignment. • Build Scalable Application and Data Layers: Design, build, and maintain our data pipelines and application to service our hundreds of users. • Bridging the Data: Partner closely with product, client services, and data science teams to transform raw pipeline outputs into meaning. • Drive Greenfield Engineering: Take complete ownership of designing, building, and launching enterprise-grade applications, moving fluidly between rapid prototyping and bulletproof production systems.