Position: Middle Devops Engineer
Company: YayPay
Experience required: 2y
English level: intermediate

Необходимые навыки
You will be working in the Production operations department, and your responsibilities will revolve around monitoring the health different components of the application, configuring monitoring and alerting in Grafana, Prometheus, AWS Cloud Watch and other tools, resolving potential issues, or redirecting the resolution of those issues to the appropriate team.
You will also be responding for finding ways to automate approaches we use for system monitoring, alerting, as well as creating ways to maximize the application uptime and reliability.

You will report to our Cloud Operations Manager and will have a chance to gain DevOps experience.
Our Cloud Stack runs on AWS and includes the following technologies:
NetflixOOS(Eureka, CloudConfig), Kafka, Redis, RabbitMQ, MySQL, ElasticSearch, EKS, ECS, EC2, S3, RDS, MSK, OpsWorks(Chef), Terraform

— You should have a least 2 years of experience on the DevOps or SRE position
— Upper-Intermediate level of English
— Experience with Grafana, Prometheus, CloudWatch or other monitoring and alerting systems
— Experience with AWS Technology Stack

Предлагаем
— Flexible working hours
— After Covid-19 — a comfortable office in Kyiv with all the standard supplies
— A high-level (A1) corporate insurance from Uniqa
— Tax compensation
— Equipment (laptops, screens etc.)
— Educational and professional development support

Обязанности
Responsibilities:
— Monitoring application components states
— Monitoring application logs
— Setting up new monitoring methods and configuring existing ones
— Setting up server state alerting and escalation system
— Come up with the ways to maximize the uptime and minimize the latency of the system overall, as well as individual services
— Provide infrastructure fixes where necessary
— Escalate issues to appropriate team members
— Suggest improvements to the systems monitoring and alerting systems
— Collecting systems KPIs and providing them to management
— Making changes to the configuration
— Perform availability, latency and performance measurements on the application
— Perform system capacity planning
— Identify and implement ways of automating different measurements and systems

**Hiring Process**
Our Hiring process consists of several stages:
— Initial screening call with HR person (20 min)
— Technical Interview with Cloud Operations Engineer (1 hour)
— Final interview with Senior Engineering Manager (30 minutes)

О проекте
YayPay is building a cutting-edge accounts receivable management software that uses machine learning to help finance teams become revenue heroes. YayPay’s vision to help finance teams perform their most valuable work. We’re a rapidly growing company at the forefront of back-office automation, AI, and machine learning. YayPay is also one of the leaders of the industry in terms of user experience and consumerization of enterprise software.

Currently, YayPay works with 25 engineers in Ukraine, mainly located in Kyiv, Dnipro, and also in other regions of Ukraine. Due to the COVI-19 pandemic, we are fully remote in all the locations of the company, but once it gets better we expect to provide office space.

From the technology perspective, YayPay Platform is a SaaS offering, which integrates with a number of different systems, such as:
— ERP systems to pull accounts receivable data from (e.g. NetSuite, Intacct, Sage X3 etc.)
— CRM systems, like SalesForce to pull and push relevant customer information
— Payment Providers to allow CreditCard and ACH to be made through the platform
— Credit Providers like CreditSafe to pull credit information

The application currently undergoes the phase of the active new feature development — we are working on building new modules: Reporting & Analytics Module, Credit Module, Cash Application Module.

The application is built on Service-Oriented Architecture, with the use of Kubernetes and technologies around it.

The main technology stack is described below:
— Back-End: Java 11, Spring Framework, Hibernate, MySQL, Kafka, Exasol
— Front-End: VueJS and KendoUI
— Machine Learning: Python and relevant machine learning libraries are used for the ML services
— Hosting: the application is hosted on AWS, with extensive use of the native AWS services