As a Cloud AI Data Engineer, you will design, build, and optimize AI-ready data pipelines across GCP, and Snowflake. You will work closely with data science, engineering, and business teams to integrate structured data, create high-quality synthetic datasets, and support scalable AI solutions through robust ETL pipelines, APIs, and backend workflows. This role requires strong hands-on expertise in cloud-native data engineering, performance tuning, cost optimization, and production-grade data systems that enable advanced analytics and AI use cases. As a Cloud AI Data Engineer, you will design, build, and optimize AI-ready data pipelines across GCP, and Snowflake. You will work closely with data science, engineering, and business teams to integrate structured data, create high-quality synthetic datasets, and support scalable AI solutions through robust ETL pipelines, APIs, and backend workflows. This role requires strong hands-on expertise in cloud-native data engineering, performance tuning, cost optimization, and production-grade data systems that enable advanced analytics and AI use cases. ### Responsibilities - Design, build, and maintain scalable AI-ready data pipelines across GCP, and Snowflake. - Develop and optimize ETL workflows to ingest, transform, validate, and integrate structured data for analytics and AI solutions. - Create and curate synthetic datasets that reflect real-world data patterns, edge cases, and business scenarios for AI model development and testing. - Build APIs and backend workflows to enable seamless data access, integration, and orchestration for AI-driven applications. - Collaborate with data scientists, ML engineers, product teams, and business stakeholders to understand data requirements and deliver reliable data solutions. - Implement monitoring, quality checks, automation, and CI/CD practices to improve reliability, scalability, and operational efficiency. - Optimize pipeline performance, storage, compute usage, and cloud costs across modern data platforms. ### Qualifications - 3–6 years of hands-on experience in data engineering, ETL development, and cloud-based data platforms. - Strong experience building AI-ready data pipelines across GCP, and Snowflake. - Expertise in designing and generating synthetic datasets that capture real-world patterns, anomalies, and edge cases. - Strong hands-on experience with ETL pipelines, structured data integration, data modeling, and data quality frameworks. - Proficiency in Python, SQL, and cloud-native services for building scalable and production-ready data systems. - Experience developing APIs, backend services, and workflow orchestration components that support AI and analytics solutions. - Good understanding of performance tuning, pipeline scalability, cost optimization, and cloud resource management. - Familiarity with CI/CD, version control, monitoring, logging, and automation practices in data engineering environments. - Strong communication skills with the ability to work effectively with technical teams, business stakeholders, and client-facing groups.