• Planned, designed, and deployed relational database and schema architecture using Snowflake. Migrated data stored in Excel, SAP, and Dodge Data & Analytics warehouse to Snowflake.• Developed Python algorithm for creation of tables, queries, and stored procedures. Used algorithm across multiple projects to reduce scripting hours.• Automated ETL/ELT processes using DigDag reducing manual effort, optimizing procedures, and logging errors.• Set up 160 Salesforce Marketing Cloud connections using Treasure Data platform, reduced 160 table cleansing and staging procedures to 7 using Python to create and standardize schemas and tables.• Designed and implemented ETL pipelines for processing large datasets, resulting in a 50% reduction in processing time and increasing data availability by 75%.• Migrated data from AWS S3 buckets to Snowflake, designed and implementeddata pipelines to generate analytics for the ConTech team.• Used Spark to connect to web APIs. Created and modified existing storedprocedures using Spark SQL, and analyzed data to create a new taxonomy for the ConTech group.

• Extracted large datasets from Excel, loaded to Power BI, transformed data using DAX, and designed analytics reports for senior management. Resulted in reduction of manual processing in Excel, easily identifiable KPIs which led to easier, efficient, and effective decision making.• Designed algorithm to predict potential revenue of commercial projects using regression and machine learning methods.• Simplified groupings of commercial projects by city and state, created data models to identify revenue potential, steered leadership and teams towards high value projects resulting in 10-25% market revenue increases.• Leveraged Power BI to gain understanding of commercial construction market using geospatial algorithms such as grouping data points using longitude and latitude leading to deep, yet clear understanding of commercial construction potential revenue. • Created bots in Power BI to assist new users while getting acclimated with reports, enabled bots to automatically filter user data based on their company role, resulting in satisfied coworkers and better strategy planning and execution.• Experimented with AWS EC2 instances, S3, and Athena to assist sales organization with data decisions.

● Identified inventory overages reducing spending by 25%● Used forecasting to recommend marketing strategies to generate business resulting in10% increase in fleet sales● Facilitated regular communication with clients to ensure their needs were met, fosteringlong-lasting relationships