About the role:

We are currently seeking a Hadoop infrastructure engineer to work in the Data Lake Hadoop

DevOps team.

Skills:

● Hadoop MapR distribution

● Automation framework: GIT / Ansible / Jenkins

● Linux admin skills

What you will do:

As part of DevOps product team you will take end-to-end care of Hadoop platform being part of

the Data Lake ecosystem, that includes operating, scaling, supporting and engineering

challenges.

● Develop: further engineer and automate the platform across technologies and

infrastructures with strong focus on network, servers and monitoring.

● Scale & harden: help to scale the platform to meet rapidly growing demand and load.

● Operate: overlook daily operations, maintenance, monitoring and capacity situation for

24x7 business critical platform.

● Support: help, troubleshoot and consult use cases, solve incidents, coordinate changes

You should have:

● Master degree in computer science or related field.

● Hands-on experience in running 24x7 critical, high load, big scale production platforms.

● Deep expertise in Hadoop mapr (HPE) distribution

● Expert knowledge on infrastructure automation using Ansible, Jenkins and Git.

● In-depth knowledge of Linux, preferably Red Hat Enterprise.

● Good experience with network and infrastructure administration.

● Hands-on experience in monitoring frameworks of Prometheus & Grafana.

● Some working experience in Docker / Kubernetes.

● Fair knowledge on Elasticsearch / Kibana.

● Some knowledge on Microsoft Azure / GCP (nice to have).

● Ability to use English in daily communication.

● Ready to learn extremely fast in a very agile and high pace environment.

The Team:

● Work in a highly skilled, highly motivated international team of unique professionals.

Learn very fast, have a high impact and start-up feeling from day one.

● We follow scrum principles, trace work on Jira, talk via slack and live high trust DevOps

culture.

Our Tech:

● Red Hat Enterprise Linux as OS layer.

● Jenkins, Ansible, Git as automation engine.

● Mapr (HPE) Hadoop cluster as storage with Hive, Spark, Drill, Hue ecosystem services.

● Data Science Workbench is based on JupyterHub and Kubeflow.

● Airflow to orchestrate data injection.

● Rancher Kubernetes Platform as underlying container infrastructure.

● Elasticsearch (OpenDistro) for central logging and specific use cases.

● Microsoft Azure and Google (GCP) for hybrid scenarios.