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Mid-level role with in-demand GCP and Snowflake skills and moderate employer brand, leading to medium competition.
Core data engineering skills transfer across industries, but GCP/Snowflake specialization makes background fit moderately sensitive.
Explicit 3–6 years plus mandatory GCP, Snowflake, Python, ETL, and synthetic data expertise indicates high strictness.
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Design, build, and maintain scalable AI-ready data pipelines within GCP and Snowflake environments.
Develop and optimize ETL workflows and synthetic datasets to support AI model development and analytics.
Implement APIs, backend workflows, monitoring, and automation to ensure reliability, scalability, and cost-efficient cloud data systems.
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 Google Cloud Platform (GCP) and Snowflake.
Proficiency in Python, SQL, and building production-grade cloud-native data systems.
Work Experience Required: 3–6 years in relevant data engineering roles.
Experienced in building and optimizing cloud data pipelines specifically for AI and analytics use cases.
Skilled in creating synthetic datasets that reflect real-world and edge case scenarios for AI model training.
Comfortable collaborating with cross-functional teams including data scientists, ML engineers, and business stakeholders to deliver reliable data solutions.