> Markdown version of [/jobs/ext/4690890-google-cloud-platform-data-engineering-technical-architect-lead](https://www.wearedevelopers.com/jobs/ext/4690890-google-cloud-platform-data-engineering-technical-architect-lead). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Google Cloud Platform Data Engineering Technical Architect/Lead - **Company:** ClifyX, INC - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Contract:** Contract - **Skills:** Java (Programming Language), Airflow, Cloud Database, Information Engineering, Software Design Patterns, Distributed Data Store, Data Processing, Google Cloud, Apache Spark, Enterprise Integration - **Published:** November 12, 2026 - **Apply:** https://www.dice.com/job-detail/8acd079b-87cf-479b-87f4-3c96cac02ba8 ## About the Role Core Skills: Google Cloud Platform, Spark, Airflow, Java , Exposure to ML is preferred, Experienced Technical Architect based in the USA to lead the architecture and technical design of scalable, cloud-based data solutions within the Retail domain. The ideal candidate will have strong hands-on expertise in Google Cloud Platform (Google Cloud Platform), Spark, Airflow, and Java, along with proven experience in designing enterprise-scale data processing and integration solutions. The role requires a combination of solution architecture, technical leadership, hands-on engineering expertise, and client-facing communication skills. ## Description * Define and drive the end-to-end technical architecture for cloud-based data and application solutions on Google Cloud Platform, Spark, Airflow, Java. * Define architecture standards, design patterns, integration approaches, security considerations, and engineering best practices. * Review solution designs and code to ensure scalability, performance, reliability, maintainability, and security. * Lead technical discussions with customers, product teams, engineering teams, and other stakeholders. * Provide technical direction and mentorship to onsite and offshore engineering teams. * Troubleshoot complex technical and performance issues across distributed data-processing environments. * Support design reviews, estimation, technical planning, deployment readiness, and production implementation.