- Design, build, and maintain Philo's core dimensional models and data marts in dbt, serving as an owner of the analytics engineering layer of our data stack
- Define and govern key business metrics and KPIs, ensuring consistent definitions and reliable calculations across all downstream reporting and analytics
- Partner with stakeholders across the organization (Finance, Product, Marketing, Ad Sales, Content, and others) to understand their data needs and translate business questions into well-modeled, performant data products
- Establish and enforce best practices for dbt project structure, model design patterns, documentation, and testing — raising the quality bar for analytics engineering at Philo
- Build and maintain comprehensive data documentation and a data catalog so that analysts, data scientists, and business users can confidently self-serve
- Implement and maintain data quality checks and monitoring (e.g., via dbt tests, BigEye, or similar tools) to proactively catch issues before they reach stakeholders
- Optimize query performance and warehouse cost efficiency in Snowflake through thoughtful model design, materialization strategies, and incremental processing patterns
- Collaborate with data engineers on pipeline design and data ingestion to ensure raw data lands in forms that support clean, efficient downstream modeling
- Drive alignment across teams on data definitions, naming conventions, and a single source of truth — building stakeholder trust in data and reporting over time
- Contribute to the evolution of Philo's overall data platform strategy, particularly as it relates to the semantic/metric layer, data governance, and analytics engineering tooling
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