• This role will focus on building scalable ETL/ELT pipelines, ensuring data integrity and compliance, and supporting advanced troubleshooting across complex VA systems. • This position transforms raw data into structured formats suitable for analysis and reporting while loading it into target databases or data warehouses. • Design and implement scalable ETL/ELT pipelines to support enterprise data integration, analytics, and reporting initiatives. • Combine data from multiple heterogeneous sources into unified, reliable data views. • Transform and load data into target databases or data warehouses using best practices for performance and scalability. • Ensure the highest possible standards of data quality, consistency, and accuracy across systems. • Monitor and report on data processes to ensure data consistency, integrity, and compliance with applicable privacy and regulatory requirements (e.g., HIPAA and federal data security standards). • Oversee daily operations, including incident management and production support. • Troubleshoot and debug complex data integration and transformation issues, performing root cause analysis across systems and pipelines. • Manage coordination between systems, teams, and stakeholders to resolve integration issues efficiently. • Support and enhance existing ETL processes and data distribution workflows. • Perform ongoing VistA table configuration and related healthcare data tasks. • Develop new data domains and optimize existing data models. • Solve complex technical challenges with innovative and strategic solutions.

• Translate business needs into data solutions and products aligned with the data platform strategy • Negotiate with stakeholders an impact-driven data strategy • Mentor Swilers on data usage and best practices to improve data literacy and promote data decentralization • Develop ETL/ELT routines and data pipelines • Implement CI/CD processes to ensure data quality • Create, manage, and document data models using dbt (Data Build Tool) to ensure data quality and efficiency • Use Metabase to create visual dashboards and reports that provide actionable insights for business teams • Design and implement efficient data storage solutions to support analytics, including Snowflake • Collaborate with cross-functional teams to understand business needs and translate them into clear technical requirements • Monitor the integrity and performance of data systems and make necessary adjustments • Ensure compliance with data security and privacy policies

• Model and document new datasets (both structured and semi-structured) to exploit the value therein across all our business units • Partner with subject matter experts to document, align and automate business metrics that define success • Work with Analysts to optimize their use of data, either through query reviews, modeling exercises, or dashboard audits • Own the design, monitoring, and deployment of certified data sources on our reporting platforms and warehouses • Insist on the highest standards for data reproducibility, auditability and compliance • Serve as a subject matter expert for data strategy thought leadership and implementation across the business • Enable non-BI&A team stakeholders to adopt data as part of their business and usual

• Partner with our technical lead to design flexible and scalable data models that capture the ontological relationships of our healthcare data, balancing immediate business needs with long-term architectural health. • Translate complex company goals into governed, reusable data models in dbt and Snowflake, serving as the "source of truth" for downstream BI tools (Hex). • Execute the design and development of robust data transformation pipelines. You will focus on modularity, performance optimization, and maintainability, ensuring our data assets scale with our data volume. • Move beyond simple schema design by implementing automated testing (data freshness, schema validation, null checks) and observability. You will ensure that stakeholders trust the data implicitly. • Partner with Data Engineering to define ingestion requirements and with Product/Customer Success to scope analytical needs, turning vague business problems into concrete technical requirements. • Collaborate with technical lead to identify technical debt and architectural opportunities, proposing solutions that align our data infrastructure with our 6-month OKRs.