Description

Key Responsibilities

• Design, develop, and maintain cloud-native applications and infrastructure on Google Cloud Platform (GKE, Cloud Run, and serverless architectures).

• Develop and optimize data engineering solutions using Databricks to support scalable and reliable data processing.

• Implement and manage CI/CD pipelines and GitOps practices to automate software delivery and deployment.

• Build and maintain APIs to support application integration and enterprise services.

• Establish artifact management processes using JFrog Artifactory for dependencies, build artifacts, and container images.

• Develop and maintain data ingestion pipelines leveraging BigQuery, Spanner, AlloyDB, and Cloud Storage.

• Design and implement event-driven architectures using Pub/Sub and Kafka.

• Automate infrastructure provisioning and configuration using Terraform.

• Ensure compliance with GCP security standards, including IAM, encryption, monitoring, and governance.

• Manage source code repositories and release pipelines using Azure DevOps.

Required Qualifications

• 5+ years of experience in DevOps, Cloud Engineering, or Software Development.

• Strong experience with Google Cloud Platform, including GKE, Cloud Run, BigQuery, Spanner, AlloyDB, and Cloud Storage.

• Hands-on experience with Databricks and cloud-based data engineering.

• Experience implementing CI/CD pipelines, GitOps methodologies, and API development.

• Experience with JFrog Artifactory and Azure DevOps.

• Proficiency in Infrastructure as Code using Terraform.

• Experience building event-driven solutions using Pub/Sub and Kafka.

• Strong understanding of GCP security, IAM, encryption, and cloud governance best practices.

• Excellent analytical, communication, and problem-solving skills.

2. Backend & AI/ML Engineer

Key Skills

Education

Bachelor's degree