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DevOps Engineer

Há 1 mês

Brazil, BR Impelsys Tempo inteiro

Hiring Immediately - Azure DevOps / Data Platform Engineer in (LATAM/Brazil, US EST Shift)


Technical Skills

  • Azure DevOps pipelines, Git repos, artifact management
  • Terraform, IaC governance patterns
  • Azure Data Lake Storage Gen2, hierarchical namespace, ACL models
  • Azure Data Factory, Databricks, Synapse, Spark
  • Azure Functions, Key Vault, networking (VNets, private endpoints, firewalls)
  • Monitoring stacks: Log Analytics, Application Insights, Azure Monitor
  • Scripting: PowerShell, Python, Bash
  • Security controls: RBAC, managed identities, secrets management, encryption
  • CI/CD patterns, release strategy design, automated testing frameworks
  • Jira, Confluence, ServiceNow


Support: US EST (Mon-Fri 9:00am-5:00pm)(Best Fit)

Roles and Responsibilities

  • Architect and maintain CI/CD pipelines for Data Lake components: Data Factory, Databricks, Functions, Synapse, Spark workloads, storage configurations.
  • Implement Infrastructure-as-Code with Terraform for provisioning storage accounts, networking, compute, identity, and security layers.
  • Enforce branching discipline, artifact integrity, automated testing, and controlled release gates.
  • Automate environment provisioning, ACL management, key rotation, lifecycle policies, and cluster configuration.
  • Integrate DevOps processes with enterprise security: RBAC, managed identities, Key Vault, private networking, encryption controls.
  • Build observability: logging, metrics, alerting, dashboards for pipelines and platform components.
  • Maintain backup, restoration, disaster-recovery patterns and test them for reliability.
  • Eliminate configuration drift through standardized templates and environment baselines.
  • Maintain and optimize agents, service connections, and deployment runtimes.
  • Perform incident response and root-cause analysis, document systemic fixes.
  • Deliver reusable automation modules for data engineering teams.
  • Optimize workload performance and cost within the Data Lake environment.
  • Ensure compliance with governance, audit requirements, and data protection mandates.
  • Drive continuous reduction of manual operational work through automation.