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Mid-tier brand, metro location, and in-demand Azure/Snowflake skills create moderate competition for senior candidates.
Cloud Data Engineering with Azure and Snowflake is moderately transferable across industries but expects platform-specific experience.
Mandatory 8+ years plus specific Azure, ADF, Snowflake, and DBT expertise tightens shortlisting significantly.
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Design, develop, and optimize ETL/ELT data pipelines using Azure Data Factory, Snowflake, and DBT targeting cloud-based data integration and transformation.
Build and maintain data workflows integrating multiple data sources into Snowflake, ensuring performance, reliability, and data quality standards.
Collaborate with stakeholders and cross-functional teams to translate business requirements into technical data solutions and oversee data governance and documentation.
8+ years of experience in data engineering roles specifically using Azure and Snowflake platforms.
Proficiency in designing and implementing ETL/ELT pipelines with Azure Data Factory and transforming data using DBT and SQL.
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
Experience working in cloud-based data environments with large-scale datasets.
Experienced in cloud-native environments focusing on Azure Cloud Platform services and Snowflake data warehousing at scale.
Capable of translating complex business requirements into technical data engineering solutions while maintaining data governance standards.
Skilled at collaboration across stakeholders including data analysts, architects, and DevOps teams to deliver scalable, optimized data pipelines.