Cloud AI Data Engineer

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.

• 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.

• 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.