Dzanan is a vibrant Software Engineer with 5+ years of technical expertise with interests centred around distributed and decentralized systems using GoLang and AWS stack on projects for clients. He has experience working in both startup and enterprise environments for local and international clients.
Creating and developing innovative software solutions for clients across a broad range of industries.
Provide guidance and technical support for backend development within the blockchain space.
Developed an innovative blockchain solution for smallholder farmers to track and manage smart farming practices; using the integrated loyalty program and FarmChange cryptocurrency.
Oversaw the efficient onboarding of 15,000+ farmers to the system during its soft launch.
Effectively developed and maintained a highly scalable backend solution for Claire IT's service platform for the automotive industry.
Used Google Cloud and Go programming to build the hexagonal architecture for Claire's solution from scratch.
Applied test-driven development principles to the data-intensive code; producing a data-intensive backend solution running large scale production processes.
Offered full stack expertise for the development of a data intensive backend solution for a local online learning startup.
Provided support for the maintenance of the Linux CentOS server on the project.
Efficiently managed both the frontend and backend processes for the client.
Participated in the development and maintenance of a custom PHP framework used by startups in the HUB387 accelerator program under the Embassy of Sweden initiative.
Worked on the setting up of successful AWS instances, load balancing with Nginx and performed large database migrations on the solution for HUB387.
Efficiently deployed a full stack framework tailored to the needs of startups to iterate and launch quickly.
Developed a solution that enables mechanic car inspections to be pushed to a blockchain platform, making odometer tampering impossible. Worked on the backend that interacted with blockchain platform. Built a fully working prototype of the solution as a tablet application for mechanics, with an admin web dashboard and a car verification/inspection page.
Wrote the machine learning algorithm on the project to train the virtual car to drive through a grid world. Implemented a Q-Learning reinforcement learning algorithm to undertake the process. This was part of Udacity’s Machine Learning Nanodegree program.
Implemented a set of numerical methods in Go on the project. The methods involved include the following: Interpolation (Linear, Lagrange), Regressions (Linear, Polynomial, Exponential), Root finding (Bisection, Newton’s method), Numerical Differentiation (Backward difference formula, Forward difference formula, Central difference formula), Numerical Integration (Trapezoidal rule integration, Simpson’s rule integration)
