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Lead DevOps Engineer

* Salário: R$ 18.000 a R$ 30.000 por mês (estimado)

* O valor exibido é uma estimativa calculada com base em dados públicos e referências do mercado. Não garantimos que este seja o salário oferecido para esta vaga específica.

Área: Tecnologia da Informação

Nível: Gerente

We are hiring a Lead DevOps Engineer to ensure the reliability and growth of our Ads Organization data infrastructure at scale. In this role you will upgrade platforms, troubleshoot production issues, and optimize CI/CD pipelines while partnering closely with stakeholders. Apply now to build dependable delivery and monitoring practices

Responsibilities

  • Drive and optimize data processing operations across Airflow/MWAA, Spark, and Flink
  • Architect and maintain AWS cloud infrastructure using Kubernetes and Terraform
  • Coordinate with stakeholders to elicit requirements and provide visibility into infrastructure changes
  • Perform upgrades, routine maintenance, and root-cause troubleshooting on data platforms with Datadog monitoring and performance insights
  • Strengthen CI/CD automation by enhancing Spinnaker and Jenkins pipelines for reliable releases

Requirements

  • Extensive experience in DevOps engineering, including 5+ years in similar roles
  • Hands-on leadership exposure with 1+ year guiding a team or owning delivery outcomes
  • Strong expertise in Amazon Web Services (AWS) for cloud infrastructure deployment and operations
  • Practical experience using Apache Airflow to orchestrate and schedule data pipelines
  • Expert-level knowledge of Kubernetes for managing and scaling containerized applications
  • Solid proficiency with Terraform for automating infrastructure and managing configuration
  • English proficiency at B2 (Upper-Intermediate) or above to collaborate effectively and report clearly

Nice to have

  • Background with Apache Flink for real-time data stream processing
  • Working knowledge of Apache NiFi for data flow automation and control
  • Familiarity with Databricks for advanced analytics and machine learning workloads
  • Hands-on use of Datadog for monitoring infrastructure and resolving issues