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Popular data-engineer role, metro location, and broad Azure/PySpark requirements create moderate applicant competition.
Data engineering skills transfer across industries, though sales and inventory domain knowledge moderately increases specificity.
Multiple mandatory Azure, Databricks, PySpark, SQL and data modeling skills indicate strict technical filters.
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Design, build, and optimize scalable data pipelines using Azure Data Factory and Azure Databricks to support sales, inventory, and business analytics.
Develop and manage SQL-based data models and integrate multiple data sources, ensuring data quality, validation, and performance optimization.
Support Power BI with optimized data models and collaborate with stakeholders on sales and inventory KPIs, including production deployment and monitoring.
Strong hands-on experience with Azure Data Factory, Azure Databricks (PySpark), and Azure Data Lake (ADLS Gen2).
Proficiency in Python, PySpark, and advanced SQL (performance tuning, query optimization).
Experience handling large sales and inventory datasets and data modeling for analytics (Star/Snowflake schema).
Work Experience Required: Not explicitly mentioned in the JD
Experienced in designing end-to-end Azure-based data engineering solutions supporting sales and inventory analytics.
Comfortable working directly with business stakeholders to translate KPIs into data models and pipelines.
Capable of independently managing deployment, monitoring, and optimization in fast-paced, client-facing environments.