Minimum7 to 12 Years of in Devops + Data Engineering related technology experience-
Mandatory
-
Expert level understanding and experience on
DevOps
area.
Expert level understanding and working experience in
Kubernetes
under below areas –
GitOps (ArgoCD)
(Nice to have) Experience provisioning data platforms like(Kubeflow, thingsboard, apache superset, Dremio, etc..)
Experience with Kubernetes resource templating with helmcharts and or kustomize
(nice to have) Helmfile
Ingress Controllers (Traefik and Nginx)
Cert-manager / Let’s encrypt.
Load Balancers
HAProxy
Hands on Experience with
Private Node Pools
KeyVault
Application Gateway
Postgresql
Eventhub
VNETs and Subnets Configuration
Virtual Machines
Container Registries
Cost Management and Billing
Entra ID
Log Analytics
Expert level understanding and working experience in IaaS(Infrastructure as a Code) – Terraform
Understanding of Docker, Docker File, Docker Registry,Automate Build
Hands on experience on Azure DevOps(CI/CD Pipeline,Releases, Project Administration, Package Registry (PIP, Maven etc)
Understanding of GitFlow and Semantic Versioning
Practitioner of AGILE methodology (Scrum/Kanban)
Knowledge and experience in Code Management, Code Versioning, Git flow, Release Planning.
Excellent communication, presentation ,documentation skills.
Mindset – taking initiatives, team player, keen to learn, adapt changes.
Good to have
Expert level understanding of distributed computing principles(Big Data Processing).
Working experience as Data Engineer in Cloud environment (
Microsoft Azure
).
Understanding of designing Data Pipelines for ETL process(Databricks/Delta table/Spark).
Good knowledge and hands on experience in Apache Spark(Batch and Streaming data).
Understanding/Experience in designing and setting up Delta lakehouse architecture (Data vault 2.0, Data mart/Star Schema, Snowflake)
Exposure to querying technologies like DremIO.
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.
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