• Ingested millions of data points from weather sources and displayed them on an analytical dashboardso that ATC controllers could provide pilots with accurate real time wind and lightning strike readings• Refactored Vue components into reusable components by combining shared attributes through propswhich reduced development time of a new web product by a few days• Auto populated advisories from FAA and NOC through a Django backend subscription service whichreduced operational users’ workload by 20%• Created a failsafe for a safety critical flight data stream by backing up to a Postgres database in theevent of message queue outages allowing users to view stale data later• Led cross-functional program sprint planning as Release Train Engineer, coordinating teams over athree-day period and presenting final objectives to Director and VP of Engineering

• Enhanced the user experience of our product offering page through effective integration of PrimeReact, achieving a 20% increase in site viewership and improved user engagement• Guaranteed customer data availability by creating database read replicas in multiple regions using AWSglobal tables• Leveraged DynamoDB, Lambdas, and S3 buckets to manage data storage and access for our RBACsystem, increasing security throughout the application

• Implemented JavaScript callback functions that would update forms with error messages as userstyped resulting in fewer mistakes by users• Developed a Python command-line tool to efficiently invoke PHP endpoints, resulting in a notable 10%increase in the speed of test run configurations• Improved legacy MySQL database by optimizing queries and tables, reducing storage by 30%• Improved Jenkins CI/CD pipelines by parallelizing independent jobs resulting in a 30% decrease inpipeline runtime

• Created comprehensive mock test scenarios for various acceptance test cases which increased testcase coverage from 40% to 90%

I worked with a Carleton University professor, a research scientist from the Defense Research and Development Canada (DRDC) and a scientist from the National Research Council (NRC) to create a PIP installable library that uses a graphical user interface (GUI) to parse data and output data collected by a Novelda X4M03 radar, a TSW1642 radar and a Ouster OS1-16 LIDAR to users. A Linux virtual environment was used to collect data for the LIDAR and a Windows environment was used to collect data for the radars.The entire project was coded in Python, some code was converted from MATLAB into Python code, documentation was done using Sphinx and GIT was used as the version control to save the source code on GitHub.