Data Engineer

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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