Position: DevOps Engineer for a product company
Company: Data Science UA
Category: DevOps
Experience required: 3y
English level: upper
Published: 2022-01-01T00:00:00+02:00

Powered by their patented machine learning technology, Whole Market View provides the comprehensive data scope and quality required by the world’s leading advertisers to precisely assess competitive opportunities at scale across their entire market, without limitations. Their index information hourly, processing over 10TB of new data, 500 million adverts, and 200 million keywords across 15 different languages each day.
Things that make this Company Unique

Machine-Learned Whole Market View
AI-Driven Data Segmentation
Automated trend detection & monitoring technology
Built for Client Value and Outcomes
World Class Customer Success

**Qualifications**:

✔️Bachelor degree in Computer Science, similar technical field of study, or equivalent practical experience
✔️Proven successful experience as a DevOps engineer
✔️Excellent communication skills
✔️Exceptional analytical, quantitative, problem-solving, critical thinking,
✔️Confident writing production-quality code (bash etc)
✔️Building and automating deployment pipelines using CI/CD tools.

**As a plus**:
✨Experience working on complex distributed systems and version control systems
✨Good knowledge of the core technologies they use
✨Experience with Ansible, Terraform, or other configuration management systems
✨Experience with Docker
✨Confident writing production-quality code preferably in Python and SQ

**Responsibilities:**
💪Automate the provisioning, management, and monitoring data platforms and architecture
💪Manage the deployment, orchestration, and monitoring of data processing and dissemination platform
💪Design and implementation of CI/CD pipelines covering infrastructure, code build, and deployment
💪Develop internal tooling and capability to support their engineers and SaaS solutions
💪Work closely with teams to create ultra-scalable, flexible, and highly reliable systems
💪Troubleshooting, diagnosing, and fixing production software issues
💪Working with AWS technologies and improving infrastructure security and resiliency
💪Creating / scripting, monitoring, and maintaining Spark Clusters
💪Building and working with complex ETL workflows (Luigi, Airflow)
💪Be empathetic towards team members and customers

**Technologies they Use across their Technology Teams:**
✔️Languages: Java, Scala, JavaScript, Python, SQL, Bash
✔️Frameworks: Spark (scala), DropWizard, React, Backbone, Akka, Play Framework (Scala), Flask
✔️Databases: PostgreSQL, AWS(S3), Redshift, Redis, MongoDB, Cassandra
✔️Technologies: RabbitMQ (messaging), Quartz scheduling, Maven
✔️CI/CD: TeamCity, Jenkins
✔️Containerisation: Docker, Kubernetes
✔️Configuration: Ansible, Terraform
✔️Monitoring: ELK, Grafana, Prometheus
✔️Source Control: Git (GitHub)
✔️Cloud: AWS

The company data team is a market leader in developing complex ETL and machine learning solutions. With published authors and award winning data scientists who contribute some of the major machine learning and distributed data technologies such as Apache Spark, they are a friendly, passionate group of engineers making a career out of building great software for their customers.
The company`s architecture is dealing with billions of data points every day, generated from over two thousand data processes running through orchestrated workflows, multiple machine learning models, distributed computations in spark, web applications, and streaming data coming in twenty-four hours a day at hundreds of times a second.
Their engineering culture is underpinned by sharing knowledge, coaching and growing together. You will have the opportunity to innovate with new technologies and make their system your own by having the freedom and responsibility that they think all engineers deserve. You will enjoy this role if you love technology, love being involved in every part of a complex architecture, working with the latest technologies, and solving problems at a large scale.