Position: QA Automation Engineer
Company: KaaIoT, Technologies LLC
Experience required: 1y
English level: intermediate

**Requirements:**
4+ years of experience in QA
Experience in testing server APIs
Solid Java knowledge, QA automation experience
QA methodologies: System Testing, Functional Testing, Integration Testing
QA Documentation hands-on experience
Good communication skills in English (verbal, written)

**Would be a plus:**
Experience in UI testing (Selenium)
Familiarity with navigating Linux environments; advanced proficiency a strong plus
Experience in testing cloud infrastructure (networking)
Experience in testing message-based systems (with Kafka, RabbitMQ or similar brokers)
Docker
Git, Jenkins, Gradle

**Responsibilities:**
Test cases creation, execution, and automation
Preparing and working with Test Documentation
Reproducing and verification of bugs reported by the clients
Work with Dev / QA team through the entire lifecycle of product releases

**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.

Real-time analytics platform for IoT. Consists of edge and cloud parts. Supports discrete and continuous metrics. Utilizes reactive approaches and non-blocking algorithms for IO and computations. Predicts anomalies in device data using machine learning.

**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:**
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.