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