I developed a data warehouse load framework to address key concerns of any data warehouse with regards to the loading of data. This framework looks to address issues such as data lineage, data quality, portability to different platforms, and rerun-ability of data loads. The framework was designed to be implemented using Python in a modularized manner, in keeping with concepts of reusability, testability, and maintenance of code.

I designed and developed, from scratch, a database to report on profit and loss of a business. However, one of the principles of the design was to bring in granular data and provide the mechanism to expose that to the end-user. As such, I provided an MS SSAS cube to the end users to access the data through dynamic Excel dashboards, whereby the end users were able to drill-down into their profit and loss numbers to get color that was previously unavailable to them. The back-end used for this project was Oracle with MS SSIS forming the ETL layer.

I developed a systems testing environment for an analytics project on top of TeamCity and MSBuild. The tool would destroy and create a database, run ETL scripts, deploy an MS SSAS cube, and run test cases off of the cube. Through this framework, we were able to test the full stack from table deployment to end user output.