Led the development of quarterly impact metric datasets for Movement’s loan officers and partner agents. These metrics drive email marketing campaigns around “impact lending” which is the company's most significant differentiator.Developed a data quality framework using AWS S3, Python, SQL, and Databricks to support an integration with Salesforce. Based on the results, a significant historical data clean-up effort was launched and multiple critical bugs were fixed. Senior management can now count on marketing and sales data to drive value for the business.Proposed and implemented a framework using AWS S3, dbt, Python, SQL, and Databricks to monitor freshness for all Movement’s data sources and to track critical pipeline ingestion times. This allowed silent failures in critical processes to be identified and slow-running pipelines to be optimized.Optimized pipelines related to loan data identified as being outside of the SLA based on the monitoring framework. Run times were reduced from 50 minutes to less than 10 minutes (80% reduction).Built new integrations using PySpark and Databricks to ingest data into Movement’s lakehouse from various sources including API, cloud storage, SFTP, relational databases, and others.

Led the analytics engineering effort for GEICO’s integration with Credit Karma’s Karma Drive telematics program. Responsible for collaborating with product stakeholders to take the project from raw JSON data to automated, high-quality BI reports using dbt, SQL, and Snowflake. Enabled the business to quantity customer conversion and provide accurate compensation to Credit Karma among other vital insights.Proposed and implemented a project to use the dbt-expectations package which expanded testing functionality. Used n-standard deviation testing to identify telematics events outside of reasonably expected values (e.g. speeding events exceeding 200 mph) among other use cases. Trained multiple other teams on how to implement so more critical data inconsistencies could be identified.Developed pipelines using Azure DevOps/yaml templates to orchestrate running dbt models and coordinate the CI/CD process.Built a new parameterized balance control framework for Azure ADLS integrations using Python, SQL, and Snowflake, which reduced the development time needed to start monitoring new sources from several hours to thirty minutes (85% reduction). The framework was integrated with multiple teams, increasing velocity across the company.Coordinated with the product team to develop new datasets and expand automated testing using dbt, SQL, and Snowflake.

Served as the lead analyst on a two-year transformation to move to a cloud-focused data infrastructure. Worked with cross-functional teams to rebuild all BI reports using dbt, Snowflake, SQL, and Tableau, increasing data availability and visibility.Provided analysis and datasets using Python and SQL to the Director of Financial Systems and other senior leaders to support the preparation of quarterly and annual financial statements.Built data quality dashboards using dbt, SQL, Snowflake, and Tableau to monitor critical financial data for senior management.Created and maintained a corporate data dictionary to serve as a centralized repository of information to assist analysts and senior management.

Prepared journal entries, reconciliations, and other financial work papers to support monthly accounting processes.