Katie Kuhne

Search Engine Client

• Architected a database and developed an application to facilitate partner trainings in the public sector further enabling partners to sell Google Cloud Products and replacing/standardizing a process that was previously done manually.

Hospitality Client

• Managed a team of developers migrating Access Databases to a modern technology stack in AWS and creating a custom application to replace the old processes. Was responsible for project engagement with technical stakeholders, user story creation, sprint planning, backlog grooming, and facilitating all agile ceremonies, and presenting to executive leaders.

Wire and Cable Client

• Used dbt to write ETL code that picked up SAP Cube data from a landing database and transformed the data to then be written out to a reporting database in Snowflake for Tableau developers to utilize.

Real Estate Client

• Architected a star schema data model to take the company from an asset centric, to a customer centric reporting model. This enabled them to report on all touchpoints, communications, and financials throughout the entire customer lifecycle using both batch and real time event processing. Data was aggregated from multiple source systems, transformed using dbt and brought into Snowflake where a dynamic data profiling method was used to fully understand and analyze the data.

Food and Beverage Client

• Used Azure Data Lakes, Databricks, and Blob storage to take unstructured data from multiple sources (Nir files, CSVs from university labs, Vcf genotype data, iPLEX/GBS data) and created a data ETL pipeline by writing custom Python code to move the data through the various staging buckets and ultimately wrote out the data to SQL DB and R Studio for end user analysis.

Mobile Office Space Client

• Extracted, transformed, and loaded data from 2 Salesforce instances and 3 legacy systems to consolidate customer records due to company acquisitions using business rules and fuzzy logic, implemented a data governance plan.

• Analyzed Big Data to determine which data points were key in decision making, used the necessary data to design, architect, create, and perform a data migration using SQL Server Management Studio to implement a new data warehouse for valuable reporting data.

• Automated processes through RPA technology (Automation Anywhere) as well as extracted and cleansed report data using Datawatch Monarch.

• Automated processes with Google scripts, managed "Marketplace Monitoring", a process that used Big Data to solve for mismatch in supply and demand by adjusting driver incentives and ride prices in specific cities increasing revenue by $250,000 in the first month alone.

• Used statistical algorithms in Python to sort users into clusters optimizing outreach to users of high significance, used Google BigQuery to gain insights into user buying behavior, wrote code used to sort website advertisements into categories.

• Data Mining with Wine Variables: Used data mining techniques in SAS Enterprise Miner to discover what the biggest factors were in distinguishing a 'good' wine from a 'bad' wine in order for sales representatives to make better wine recommendations

• Facebook Advertising Optimization: Used text mining techniques in combination with POS and Facebook data to make recommendations on what to include in paid advertisements, as well as when to, and when not to use paid advertising on Facebook in order to reach the largest population possible while also minimizing their spending