Position: Manual QA Engineer
Company: KaaIoT
Category: QA
Experience required: 2y
English level: intermediate

3+ years experience in manual QA
Hands-on knowledge of software test planning, estimating, and test case creation
Experience in designing and executing effective test cases
Hands-on professional experience with test management tools
Experience with Postman, DevTools
Able to work on their own to research problems or blockers
Understanding of Scrum / Agile methodologies
Structured approach and attention to details
Able to communicate effectively within the team
Good written and verbal communication skills in English
Would be a plus:

Master’s Degree in STEM (science, technology, engineering, and mathematics) or a relevant technical area
ISTQB certification
Working experience with UAT
Experience with Jira, Xray
Responsibilities:

Analysis of system requirements and areas of risk
Test planning, test case design, and test execution for web-based and client-server applications to verify that the software is meeting system requirements and specifications
Discover reproducible steps for observed and reported bugs
Log defects and track defect status, working with developers to reproduce and resolve defects, and execute re-tests and regression testing
About the project:

World leader in engines and propulsion systems and distributed power generation plants. The company develops and produces high-speed engines and propulsion systems for ships and heavy land, rail and defense vehicles, as well as drive systems for use in the oil and gas industry and in power generation.

The main area of the project:

Fleet management and engine performance monitoring;
Telematics tracking, geofencing, routing;
Fuel quality and consumption management;
Workload management and scheduling;
Predictive maintenance based on previously detected engine issues.
Problem:

The company is able to receive data from vessels, rail machinery, nuclear reactors, power plants, and mining equipment, but has issues with any further actions(transforming, processing, transferring) with that data.
The main thing their solutions were doing - creating simple data reports and data visualization that basically just including the raw data along with building predefined charts for technical analysts.
Solution:

We are building a cloud-agnostic solution that can be deployed to different cloud providers in different regions, also including deployment on a barebone hardware
The platform computes live-streaming data that incoming from the sensors connected to the assets. When the platform gets the asset data, it’s visualizing and analyzing on the UI.
Predictive maintenance implemented with help of machine learning, configurable alerts, and thresholds.
Configurable alerts can be set up with a wide system of rules and alert templates for faster and more efficient data analysis.
Our team responsible for the entire engineering cycle, from the initial device’s software design to ground-up implementation, testing automation, analytics, and UI production rollout.

Overall project technology stack:

Python, Java, Spring cloud, Oauth2, Gradle, Helm charts, Kubernetes, Typescript, Javascript, C, STOMP, DSL, FFT, Cloud providers like GCP, Azure