• Enable secure, scalable, and efficient data exchange between federal client and external data sharing partners using Databricks Delta Sharing. • Support the design and development of data pipelines and ETL routines in Azure Cloud environment for many source system types including RDBMS, API, and unstructured data using CDC, incremental, and batch loading techniques. • Conduct data profiling, transformation, and quality assurance on structured, semi-structured, and unstructured data. • Identify underlying issues and translate them into technical requirements. • Assist in building and optimizing data lakes, feature stores, and data warehouse structures to support analytics and machine learning. • Prepare, structure, and validate data for data science and MLOps workflows, ensuring it meets the quality and format requirements for modeling. • Help monitor and maintain the flow of data across BI dashboards, analytics environments, and machine learning pipelines. • Engage directly with clients and stakeholders to understand data needs and translate them into scalable solutions. • Collaborate with UX designers, business analysts, developers, and end users to define data and reporting requirements • Work with external data partners to determine their data product needs and work within the Databricks platform to enable rapid prototyping and extensible use cases • Meet with government employees at executive levels, platform stakeholders, and vendor partners. • Work within Agile teams to support iterative development, backlog grooming, and sprint-based delivery. • Provide mentorship to junior resources.

• 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.