- Act as the team architect by leading system design reviews, offering recommendations, conducting comprehensive peer reviews, and demonstrating expert-level proficiency in Python and AWS services
- Architecting and implementing scalable data ingestion pipelines, including incremental ingestion strategies for large-scale healthcare datasets
- Developing reusable, configuration-driven, containerized pipeline components and toolsets that diverse engineering profiles can use and maintain
- Work collaboratively with cross-functional teams to ensure their data requirements are met through ETL components
- Designing and maintaining data transformation pipelines using dbt, including utilizing core concepts like macros, incremental models and dbt tests
- Building monitoring and alerting systems for data ingestion processes and pipeline health
- Applying software engineering best practices including test-driven development and modular design to data infrastructure, including refactoring existing ingestion processes to improve scalability and operational efficiency
- Provide technical guidance, mentorship to junior engineers, and promote best practices and coding standards
- Champion AI-assisted development across the team - establishing norms, workflows, and expectations for using AI coding tools (e.g., Claude Code, Cursor, Copilot) to generate, iterate, and ship production-quality code
- Model the “builder to reviewer” shift - demonstrating how senior engineers direct AI agents to produce full solutions, then apply rigorous review, testing, and judgment to own the output
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