Data analyst with 4 years turning product and marketing data into decisions across SQL, Python, and Tableau. Comfortable querying Snowflake, building dashboards stakeholders actually use, and designing A/B tests. Shipped a self-serve reporting layer that cut ad-hoc data requests by roughly 60 percent.
- Wrote and optimized SQL against a Snowflake warehouse, cutting a core revenue model from a 12-minute query to under 90 seconds with CTEs and indexed staging tables.
- Built 8 Tableau dashboards on a dbt-modeled layer, replacing weekly manual reports and reducing ad-hoc data requests to the team by about 60 percent.
- Designed and analyzed A/B tests on the checkout flow in Python, identifying a variant that lifted conversion 7 percent and presenting the result to product and marketing leads.
- Automated a daily KPI pipeline with Python and scheduled SQL, eliminating roughly 6 hours of manual spreadsheet work each week.
- Pulled and cleaned marketing data from Google Analytics and BigQuery, building Power BI reports that tracked campaign ROI for 5 stakeholder teams.
- Wrote SQL to segment a 2M-row customer table, surfacing a churn signal that informed a retention campaign credited with a 4 percent reduction in cancellations.
- Cleaned and validated incoming data in Excel and Python (pandas), cutting reporting errors flagged downstream by standardizing 15 recurring data checks.
- Documented metric definitions in a shared data dictionary, reducing back-and-forth over conflicting numbers across teams.
Languages: SQL, Python, R · BI and visualization: Tableau, Power BI, Looker · Databases and warehouses: Snowflake, BigQuery, PostgreSQL · Analysis: A/B testing, statistical analysis, forecasting · Data prep: Excel, pandas, dbt, ETL · Tools: Git, Jupyter, Google Analytics
- Microsoft Certified: Power BI Data Analyst Associate (PL-300), Microsoft
- Google Data Analytics Professional Certificate, Google