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Mid-level, in-demand data engineering role with common experience band and moderate company brand.
Core data engineering skills broadly transferable, but Snowflake/GCP and synthetic-data requirements increase domain specificity.
Explicit 3–6 years plus mandatory GCP, Snowflake, Python, ETL, and synthetic-data expertise.
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Design, build, and maintain scalable AI-ready data pipelines and ETL workflows across Google Cloud Platform (GCP) and Snowflake.
Create and curate synthetic datasets reflecting real-world data patterns to support AI model development and testing.
Develop APIs and backend workflows to enable data access, integration, and orchestration for AI-driven applications with focus on performance tuning and cost optimization.
3–6 years of hands-on experience in data engineering, ETL development, and cloud-based data platforms.
Strong experience with AI-ready data pipelines on GCP and Snowflake.
Proficiency in Python, SQL, and cloud-native services for building production-grade data systems.
Experience in designing and generating synthetic datasets, ETL, API development, performance tuning, and cloud resource management.
Experienced in integrating structured data and building robust, scalable data solutions that serve AI and analytics use cases.
Capable of operationalizing data pipelines with monitoring, CI/CD, automation, and cost optimization focus.
Able to collaborate effectively with cross-functional teams including data scientists, ML engineers, and business stakeholders on AI-focused projects.