• Built a unified metric data mart in Python and SQL for TurboTax and QuickBooks, consolidating

30 metrics from disparate sources to enable executive-level reporting and operational

decision-making

• Automated data mart operations by dynamically generating SQL through Python, with table

creation and modification fully driven by config files; workflows orchestrated via SuperGlue

• Created a new KPI for TurboT ax Live by developing ETL pipeline from raw clickstream data

using Databricks SQL on AWS; output influenced product engagement strategies

• Own and maintain 20+ production pipelines supporting 50+ analytics and data science users,

with responsibilities including monitoring, debugging, SLA management, and ongoing pipeline

optimization

• Reduced manual A/B testing workload by 75% for the Mint website by developing an

automated reporting pipeline in Python and SQL, increasing experiment velocity and reducing

analyst overhead

• Developed and measured new KPI through Databricks SQL, leveraging raw clickstream data in AWS data lake, and making KPI widely available via Tableau

• Cut down manual A/B testing reporting tasks by over 75% by spearheading automated testing pipeline leveraging Python & SQL

• Created feature adoption framework & ETL pipeline leveraging Databricks SQL, Github, & SuperGlue

• Used Python to statistically measure website performance using numpy, pandas, and matplotlib libraries

• Implemented Paid Marketing data automation using SQL, Tableau, Tableau Prep, and Panoply, allowing for real-time reporting and reduced reporting times by 3-5 hours/week

• Applied LTV framework to digital marketing campaigns using T-SQL, reducing wasted ad budget by 20%

• Increased A/B testing coverage by 10% on website by implementing custom Python module & collaboration with web development department

• Utilized Google Analytics and Google Optimize to execute and analyze over 20 A/B tests on website