Travis Larson Email & Phone Number

California, United States

San Francisco, California, US

• Led the redesign of an ETL email marketing data pipeline built on Airflow, API, Python, DBT, Snowflake and SQL, resulting in a 30% increase in targetable audience and a 75% increase in performance.
• Architected and drove the automation of a physical mail data selection processing pipeline, using Airflow, Python, DBT, Snowflake and SQL, reducing time spent by +50% and reducing manual error.
• Researched and assisted in the deployment of a data observability tool Monte Carlo, improving data monitoring coverage from 10% to 100% on critical Snowflake tables.
• Owned Braze email marketing data pipeline by maintaining batch and streaming user source of truth ETL, ensuring high data accuracy and regularly meeting with Marketing to make sure needs are met.
• Oversaw a data warehouse ETL pipeline that consisted of many data sources, both structured and semi structured, using Airflow, DMS, Terraform, S3 data lake, Snowpipe, Snowflake and Looker.

San Francisco, California, US

• Orchestrated the migration of +70 tables from an external AWS s3 storage into a Snowflake data warehouse, enabling new analytics and reporting capabilities for internal stakeholders.
• Developed a data pipeline for a new credit card product by collaborating with key stakeholders and using tools such as SQL, DBT and Looker, establishing a foundation for self serve data.
• Created a SQL script that generated DBT and LookML files based on table schema, reducing time spent on each file by +90%.
• Constructed and refined hundreds of ETL tables using data modeling techniques, JSON, DBT, SQL, Snowflake and Looker, supporting company wide analytics and reporting.

Burlingame, CA, US

• Proactively found opportunity and implemented Python and VBA scripts that reduced manual report work by 30 hours per month.
• Assisted in reducing manually reviewed data from 9% to 6% by using Python, Jupyter Notebooks, Matplotlib to identify areas of max ROI then collaborating with developers to implement and test changes.
• Established a real time locally hosted data stream dashboards using Python, Dash, SQL and IIS, illustrating critical data ingestion health.
• Identified opportunities, taught myself, implemented and got value out of the following tools: PowerBI, VBA, Pandas, Matplotlib, Python and Jupyter Notebook.