GCP Tooling and GenAI Engineer

Key Responsibilities:

• Ensure high availability, reliability, and scalability of managed orchestration platforms (Astronomer & Composer) supporting data integration workflows.
• Apply SRE principles including defining and tracking SLIs/SLOs, setting up alerting and monitoring systems, and managing incident response and postmortem analysis.
• Build and maintain Terraform modules for automated and repeatable provisioning of Composer/Astronomer environments
• Develop Python-based automation tooling to streamline platform operations such as deployment automation, environment configuration, incident detection, and remediation.
• Integrate generative AI models and workflows for enhanced operational intelligence—such as AI-driven anomaly detection, automated diagnostics, predictive maintenance, and intelligent runbook generation.
• Collaborate with data engineering, infrastructure, and security teams to onboard new projects, implement best practices, and ensure platform compliance with security and governance policies.
• Enhance CI/CD pipelines for deployment of platform components, enabling robust quality gates, automated testing, and rollback capabilities.
• Maintain clear and comprehensive documentation, runbooks, and knowledge bases enriched with AI-powered insights to aid platform support and incident resolution.

Programming Languages:

• Python: This is explicitly mentioned for building automation tooling, streamlining platform operations, and developing intelligent automation and monitoring solutions. It's also implied for integrating generative AI models and workflows.

Key Technologies & Platforms:

• Apache Airflow (Astronomer / Google Cloud Composer): These are core data orchestration services that the role will support, ensure reliability for, and manage.
• Google Cloud Platform (GCP): Composer is a GCP service, and the mention of "cloud resources" and "Google Cloud Composer" strongly implies familiarity with GCP.
• Terraform: This is specified as the Infrastructure as Code (IaC) tool for provisioning and managing cloud resources, and for building and maintaining modules for Composer/Astronomer environments.
• Generative AI Technologies/Models: Gemini,Langchain,ADK,Python
• CI/CD Pipelines: Experience with enhancing these pipelines for deployment of platform components is required, implying familiarity with tools like Jenkins, GitLab CI/CD, GitHub Actions, or Tekton

•
• Monitoring and Alerting Systems: The role requires setting up and managing these, indicating a need for experience with tools like Dynatrace,Prometheus, Grafana, Datadog, Splunk, or similar

Key Technologies & Platforms:

• Apache Airflow (Astronomer / Google Cloud Composer): These are core data orchestration services that the role will support, ensure reliability for, and manage.
• Google Cloud Platform (GCP): Composer is a GCP service, and the mention of "cloud resources" and "Google Cloud Composer" strongly implies familiarity with GCP.
• Terraform: This is specified as the Infrastructure as Code (IaC) tool for provisioning and managing cloud resources, and for building and maintaining modules for Composer/Astronomer environments.
• Generative AI Technologies/Models: Gemini,Langchain,ADK,Python
• CI/CD Pipelines: Experience with enhancing these pipelines for deployment of platform components is required, implying familiarity with tools like Jenkins, GitLab CI/CD, GitHub Actions, or Tekton

•
• Monitoring and Alerting Systems: The role requires setting up and managing these, indicating a need for experience with tools like Dynatrace,Prometheus, Grafana, Datadog, Splunk, or similar

Key Responsibilities:

• Ensure high availability, reliability, and scalability of managed orchestration platforms (Astronomer & Composer) supporting data integration workflows.
• Apply SRE principles including defining and tracking SLIs/SLOs, setting up alerting and monitoring systems, and managing incident response and postmortem analysis.
• Build and maintain Terraform modules for automated and repeatable provisioning of Composer/Astronomer environments
• Develop Python-based automation tooling to streamline platform operations such as deployment automation, environment configuration, incident detection, and remediation.
• Integrate generative AI models and workflows for enhanced operational intelligence—such as AI-driven anomaly detection, automated diagnostics, predictive maintenance, and intelligent runbook generation.
• Collaborate with data engineering, infrastructure, and security teams to onboard new projects, implement best practices, and ensure platform compliance with security and governance policies.
• Enhance CI/CD pipelines for deployment of platform components, enabling robust quality gates, automated testing, and rollback capabilities.
• Maintain clear and comprehensive documentation, runbooks, and knowledge bases enriched with AI-powered insights to aid platform support and incident resolution.

Programming Languages:

• Python: This is explicitly mentioned for building automation tooling, streamlining platform operations, and developing intelligent automation and monitoring solutions. It's also implied for integrating generative AI models and workflows.