Product architect and full stack developer.

Ideated, designed and built a web testing (nodejs, express and jquery) tool for Xactly which automates UAT and Regression testing for Xactly SPM implementations.- Significantly faster SIT test cycles- Reduce UAT cycles by several days- Instant regression test results- Automates all testing and test history Created ICM Debugger tool from idea, design to deployment using RESTful API for IBM ICM that makes debugging fast and efficient.Designing, Developing and Testing custom coded solution modules including web forms and external scripts.Lead all aspects of solution delivery. Mentor and lead team of consultants.

Worked closely with Product Development team on IBM ICM roadmap. Developed POCs for new product features which were included in the subsequent releases.Contributed to the best practices and standards for IBM ICM maintained by technical solutions team.Responsible for the more customizable aspect of solution involving scripting in JavaScript, Powershell.Technical governance by conducting requirements, design, and build reviews on large-scale projects.Analyse execution plans, indexing with an aim to improve batch process and view performances.Work with client IT on configuring product security, authentication, upgrades.

Requirement Discovery Sessions with key business representatives.Design and Development of key deliverables including data model, compensation plans, reports and webforms.Worked with key client business and IT representatives on a multiphased implementation of Cognos ICM.Led all elements of projects from Discovery, Design, Build, through implementation of theircomplex web form based work flows.

-Informatica Developer at Shopper's Drug Mart-Microstrategy Consultant at Fraser Health-Microstrategy Consultant at Wind Mobile-Data Stage Developer at Canadian Tire Corporation-QA at Canadian Institute for Health Information

I worked as a research assistant while doing MSC in electrical and computer engineering.I was part of the research group FANFARE , The project is aimed at developing a system to fulfill the need for a wearable device to collect data for fall and near-fall analysis and different movement classification in general. The system consists of a computer and a wireless sensor network to measure, display, and store fall related parameters such as postural activities and heart rate variability. Machine learning algorithms (Naïve Bayesian Classifier) are used to classify different movements. The prototype has high classification accuracy in real time application. My work involved collection of data, use of machine learning and data mining techniques to classify movement and detect fall/near fall, and subsequent testing of the device.