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General Data Engineer role with broad Azure/Databricks requirements and popular mid-level profile increases applicant competition.
Core data engineering skills are transferable, but retail POS domain and specific Azure/Databricks expertise raise domain sensitivity.
Many mandatory platform and tooling requirements (Databricks, Azure, Spark, SQL, Python) increase filtering strictness.
Design, build, and maintain automated ingestion pipelines and multi-source integration on Databricks and Azure data stack for enterprise POS and commercial data.
Implement data quality validation, monitoring, incident response, and continuous pipeline improvements to ensure reliable and secure data workflows.
Develop ingestion side of attribution crosswalk, data preparation, CI/CD automation, and maintain documentation and technical metadata for enterprise data 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 building data engineering solutions on Azure cloud platform, including Databricks.
Advanced skills in Databricks, Azure Data Factory, Data Lake, Synapse, Analysis Services, SQL/T-SQL, Python/Scala, RESTful API development, and data pipeline operations.
Work Experience Required: Substantial hands-on experience with data engineering solutions on major cloud platform; exact years not explicitly mentioned.
Experienced in operating and supporting production data pipelines end-to-end in cloud environments, especially Azure and Databricks.
Strong knowledge of Lakehouse architecture (medallion architecture), CI/CD, DevOps automation, and Kimball dimensional modeling.
Domain experience or familiarity with retail, consumer goods, or manufacturing analytics involving POS, sell-in, inventory, or third-party retail data is preferred.