Google Cloud Data Engineer

Position Overview We are seeking an experienced Data Engineer to design, build, and maintain scalable data platforms and processing solutions within a Google Cloud environment. The role involves translating business requirements into reliable, secure, and high-performing data solutions that support analytics, reporting, and data-driven initiatives across the organization. Key Responsibilities Design, develop, and maintain scalable ETL/ELT pipelines using Google Cloud technologies. Build and optimize data models and warehouse structures to support large-scale analytical workloads. Implement and support both batch and real-time data ingestion frameworks. Apply DataOps practices to improve data quality, monitoring, testing, and operational efficiency. Develop and maintain CI/CD processes for data platform deployments. Automate infrastructure provisioning and management using Infrastructure as Code (IaC) methodologies. Monitor, troubleshoot, and optimize production data environments to ensure performance, availability, and reliability. Collaborate with cross-functional stakeholders, including engineering, analytics, and business teams, to deliver data solutions. Ensure adherence to security, governance, compliance, and data protection standards. Support containerized workloads and orchestration platforms where required. Contribute to the continuous improvement of data architecture, engineering standards, and platform capabilities. Qualifications Experience Minimum of 5–8 years of experience in Data Engineering, Cloud Engineering, or related disciplines. Proven experience delivering end-to-end data solutions in a Google Cloud Platform environment. Experience working with enterprise-scale data platforms and complex data ecosystems. Preferred Certifications Professional-level Google Cloud certifications in Data Engineering, Cloud Architecture, DevOps, or Application Development are advantageous. Technical Requirements Cloud and Platform Expertise Strong hands-on experience with Google Cloud data services, including data warehousing, data processing, orchestration, and messaging technologies. Understanding of cloud networking concepts such as virtual networks, subnetting, load balancing, and firewall configurations. Knowledge of cloud security principles and best practices for data environments. Engineering and Automation Experience implementing CI/CD pipelines for data engineering solutions. Hands-on experience with Infrastructure as Code tools, such as Terraform. Familiarity with containerization and orchestration technologies, including Docker and Kubernetes. Proficiency in source code management and version control practices using Git. Data Engineering Practices Strong understanding of DataOps principles and automated data quality processes. Experience designing and supporting high-volume, enterprise-scale data pipelines. Exposure to regulated or highly governed environments is an advantage. Key Competencies Strong analytical and problem-solving skills. Ability to work effectively in cross-functional teams. Excellent communication and stakeholder management capabilities. Commitment to delivering scalable, reliable, and secure data solutions. Originally posted on Himalayas