- Design, build, and maintain high-throughput, event-driven data services and orchestration pipelines using Python, Airflow, and Snowflake.
- Architect and evolve core platform components to ensure scalable, secure, and extensible data pipelines, including modernizing legacy ingestion patterns.
- Collaborate on the development and adoption of DataOps best practices such as data modeling, CI/CD, and automated testing.
- Define and evangelize data engineering standards, providing implementation guidance and code reviews for other engineers.
- Lead agentic development maturity by increasing the automation of engineering work through AI-assisted workflows.
- Shape data governance strategy, ensuring safe and consistent data access and query patterns.
- Manage cost efficiency for Snowflake and pipeline infrastructure, balancing performance with budget.
- Mentor data engineers and data analytics engineers to foster technical growth and architectural alignment.
- Lead cross-functional initiatives spanning infrastructure, data services, and observability.