Reinhardt Eiselen

Cloud Architect

Cape Town

Summary

Strong leader and problem-solver dedicated to streamlining operations to decrease costs and promote organizational efficiency. Uses independent decision-making skills and sound judgment to positively impact company success. Collaborative with dedication to partnering with coworkers to promote engaged, empowering work culture. Documented strengths in building and maintaining relationships with diverse range of stakeholders in dynamic, fast-paced settings.

Overview

years of professional experience

Certificates

Work History

Cloud Solution Architect

Aleios

06.2023 - Current

Defined cloud architecture for both hybrid and non-hybrid cloud solutions.
Identified and remediated single points of failure and security risks.
Interfaced directly with customers, stakeholders and end-users regarding capability architectures, requirements use cases and stories to derive, develop and decompose next-cycle updates and specifications.
Collaborated with development and operations teams to develop practical automation solutions and custom modules.
Provided solution designs to meet system strategies, capabilities and technologies and to respond to current and future business requirements.
Lead and mentored teams of developers to build the chosen solution for clients

Cloud Support Engineer (Big Data)

Amazon Web Services

12.2019 - 06.2023

Working with customers to solve their technical issues when making use of the AWS Big Data services through a variety of contact methods
Mentoring and developing new hires
Interviewing candidates
Working on internal projects by developing and maintaining of these projects
Performing Customer engagements, where we teach customers about the service and provide architectural guidance to them.

Education

Bachelor of Science - Information And Knowledge Systems

University of Pretoria

Skills

AWS Services

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Certification

AWS Solutions Architect Associate

Additional Information

AWS Solutions Architect Professional

Projects

Software Engineering final year project (2019), To aid rangers in anti-poaching patrolling, a system was developed to automatically create an optimized drone flight plan based on animal tracking information and reserve features. This was accomplished with the help of machine learning, specifically linear regression and classification. The project aimed to increase the probability of drones covering key areas with endangered wildlife, while being random enough to prevent poachers identifying patrol routes. Rangers and drone pilots were empowered with useful information through this dynamic application., https://github.com/cos301-2019-se/Wildlife-Drones-and-Flight-Plans