Cameron Maher

Led the design, rollout, and adoption of a company-wide semantic layer using dbt and Snowflake, standardizing metric definitions and enabling self-service analytics. Built churn prediction with Snowflake ML and added data health/freshness and ML observability to improve trust and adoption of predictive insights.

Built and maintained production data pipelines using SQL, Python, Airflow, dbt, and AWS to support core analytics and reporting. Improved pipeline reliability and data quality by implementing auditing and monitoring with Monte Carlo to reduce time to detect and resolve data issues.

Designed, built, and maintained end-to-end ETL pipelines using Python, SQL, PySpark, Airflow, Databricks, AWS, and GCP across multiple business units. Implemented data validation and monitoring to reduce recurring issues and managed database administration and governance processes to maintain compliance and data integrity.

Automated recurring data aggregation and reporting workflows using R, Python, SQL, Google Sheets, and Airflow to reduce manual effort and improve data accuracy. Built ad-hoc analyses and dashboards for product, marketing, content, and advertising teams, translating business questions into actionable recommendations.

Managed and analyzed a database of partners, prospects, and customers, creating persona-based segmentations to improve targeting and campaign relevance. Defined and reported key digital marketing performance metrics and built multi-channel engagement scoring models to identify high-intent accounts for sales.

Earned an M.S. in Statistics from San Jose State University.

Earned a B.S. in Economics from New York University, with a minor in Mathematics.