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