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Popular mid-level Data Engineer role, metro hiring, and 2+ years experience increase candidate competition.
Snowflake/SnowPro vendor specificity raises domain bias, but core data engineering skills remain transferable.
Requires SnowPro certification plus mandatory Snowflake and PySpark experience, creating strict screening filters.
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 (business, data scientists, analysts) to translate data requirements into technical solutions ensuring data quality, integrity, and compliance.
Bachelor’s or Master’s degree in Economics, Statistics, Engineering, or related quantitative field from a top-tier university.
Minimum 2+ years hands-on experience with Python and PySpark including performance tuning and complex query optimization.
Hands-on experience with modern data architectures and cloud data platforms such as Snowflake (SnowPro Core certification required), Databricks, BigQuery, or Redshift.
SnowPro Core certification required; SnowPro Advanced certification preferred.
Experienced in migrating legacy data systems to cloud-based AI-ready platforms with strong focus on semantic layers.
Proficient in managing end-to-end data pipeline development with measurable impact on analytics and AI workloads.
Able to effectively communicate complex technical details to non-technical stakeholders ensuring alignment across teams.