Cloud AI Data Engineer

Job Description: 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.