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Popular Data Engineer role with metro hiring and unspecified mid-level experience increases applicant density.
Core data engineering skills transfer across industries, though retail POS domain knowledge is a moderate preference.
Multiple mandatory technical requirements (Databricks, Azure, Spark, SQL, Python, CI/CD) imply strict technical screening.
Design, build, and maintain automated ingestion pipelines and data integrations on Azure and Databricks for enterprise POS and commercial data.
Implement and monitor data quality, validation, and automated notifications to ensure pipeline reliability and performance.
Develop and support attribution crosswalk ingestion, Lakehouse data solutions, and CI/CD pipelines for data workflows including documentation and lineage management.
Bachelor's or Master's degree in Computer Science, Engineering, Data Analytics, or related field, or equivalent experience.
Substantial hands-on experience designing and delivering data engineering solutions on Azure ecosystem and Databricks.
Advanced expertise in Databricks, Azure Data Factory, Data Lake, Synapse, RESTful API development, SQL/T-SQL, Python, and familiarity with medallion architecture and Lakehouse principles.
Work Experience Required: Substantial hands-on experience with production pipeline delivery and operational support; explicit years not specified.
Experience with production data pipelines in enterprise environments using Azure and Databricks, especially involving retail POS or commercial data.
Proven capability in full pipeline lifecycle including ingestion, quality control, DevOps automation, and data modeling (Kimball dimensional modeling preferred).
Comfortable working independently with minimal oversight on complex data engineering tasks, and collaborating with architects and BI teams in a structured engineering organization.