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Popular Data Engineer role with common mid-level profile and known company, moderate applicant density.
Core cloud data engineering skills transfer across industries, though retail POS domain knowledge is preferred.
Multiple mandatory platform skills (Databricks, Azure, SQL, Spark, Python) create strict technical filtering.
Design, build, and maintain automated ingestion pipelines and multi-source integration on Databricks and Azure data stack for POS and commercial data.
Implement data-quality checks, validation, monitoring, and automated notifications to ensure pipeline performance and reliability.
Build ingestion side of attribution crosswalk and master data foundations as per enterprise architecture specifications, supporting critical analytical models and data workflows.
Bachelor's or Master's degree in Computer Science, Engineering, Data Analytics, or related field (equivalent experience considered).
Substantial hands-on experience designing and building data engineering solutions on Azure ecosystem, including Databricks, Data Factory, Data Lake, Synapse.
Advanced SQL/T-SQL and Python (or Scala) proficiency, with strong experience in ETL/ELT design and RESTful API development for data ingestion.
Work Experience Required: Substantial hands-on experience with production data engineering pipelines; exact years not explicitly mentioned in the JD.
Experienced with medallion architecture and Lakehouse principles, including ACID storage, schema enforcement, and versioning.
Familiarity with retail or consumer goods analytics environments focused on POS, sell-in, and inventory data is preferred.
Capable of independently managing complex data engineering workloads with strong delivery focus and ability to collaborate closely with Data Engineering Leader and Enterprise Data Architect.