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Medium — metro data engineer role, popular title but requires Snowflake certification and cloud experience.
Low — core data engineering skills are transferable across industries despite Snowflake preference.
High — mandatory SnowPro certification and specific Snowflake/PySpark/cloud skill requirements.
Design, build, and maintain scalable data pipelines supporting analytics, reporting, and AI/ML use cases.
Lead migration of legacy data architectures to modern, cloud-based AI-ready platforms, including development of semantic layers for business-friendly data access.
Collaborate with stakeholders and ensure data quality, integrity, and compliance through governance and validation practices.
Bachelor’s or Master’s degree in Economics, Statistics, Engineering, or related quantitative field from a top-tier university.
2+ years hands-on experience with advanced Python and PySpark focusing on performance tuning and query optimization.
Hands-on experience with modern data architectures like data lakes, lakehouses, and warehouses (Snowflake, Databricks, BigQuery, Redshift).
Snowflake certification required: SnowPro Core mandatory; SnowPro Advanced preferred.
Experienced in leading complex cloud data migrations and developing AI-ready data platforms and semantic layers.
Able to translate business data requirements into technical solutions, collaborating effectively with cross-functional teams (data scientists, analysts, product teams).
Comfortable working with strict data quality, security, and governance standards in scalable data architecture environments.