• Build and maintain dbt models that transform raw data from multiple sources into clean, tested, well-documented datasets.
• Partner with data scientists, engineers, and product teams to translate ambiguous questions into durable data models rather than one-off queries.
• Improve data quality across the stack by writing tests, defining expectations for critical models, and triaging issues when something looks wrong.
• Document models, metrics, and lineage so engineers, analysts, and partner organizations can self-serve with confidence.
• Use Python where it's the right tool — for orchestration, ad-hoc work, or transformations that don't belong in dbt.
• Raise the bar on analytics engineering practices, including code review, modeling conventions, CI for data, and documentation standards.
• Perform additional engineering and data duties as needed to support the broader team.