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.