Google Cloud Data Engineer

Our Clients; Taguig; On-site; PHP 1,200,000 - 1,800,000; Full time

Job summary

Our Clients is seeking an experienced Data Engineer to design, build, and maintain scalable data platforms within a Google Cloud environment. Responsibilities include developing ETL/ELT pipelines, optimizing data models, and implementing DataOps practices.

The ideal candidate will have 5-8 years of experience in Data Engineering, with strong proficiency in Google Cloud services, CI/CD processes, and cloud automation tools. This role offers an opportunity to collaborate with cross-functional teams to deliver reliable data solutions.

Qualifications

• Minimum of 5–8 years of experience in Data Engineering or related fields.
• Proven experience delivering data solutions in a Google Cloud environment.
• Understanding of cloud networking and security principles.

Responsibilities

• Design and maintain scalable ETL/ELT pipelines using Google Cloud.
• Optimize data models for large-scale analytics.
• Develop CI/CD processes for data platform deployments.
• Monitor and troubleshoot production data environments.

Skills

ETL/ELT pipelines

Google Cloud Platform

CI/CD processes

DataOps practices

Containerization technologies

Infrastructure as Code

Data architecture

Tools

Terraform

Docker

Kubernetes

Git

Job description

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.