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Mid-level senior data role, Fortune-500 brand, metro location, and broad applicant pool increase competition.
Core data engineering skills are transferable, but Azure/Microsoft Fabric specialization reduces portability somewhat.
Explicit 6–10 years plus mandatory Azure/ADF/PySpark and data-platform requirements make filters strict.
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Lead design and architecture of enterprise-scale data pipelines and ETL/ELT solutions using Microsoft Fabric, Azure Data Factory, and PySpark.
Own modern data warehousing architecture, dimensional modeling, and lifecycle management for analytics platforms, ensuring performance tuning and query optimization.
Manage platform administration, monitoring, security, and cost optimization of Azure-based data services while driving engineering standards and mentoring team members.
6–10 years of experience in Data Engineering with ownership of large-scale data platforms and technical leadership.
Proficiency in advanced SQL, data warehousing architecture, dimensional modeling, and Azure data services including Microsoft Fabric and Azure Data Factory.
Bachelor's degree in Computer Science, Information Technology, or related field.
Hands-on experience with Python and PySpark for distributed data processing.
Experienced in cloud-based data platform architecture and operational management, especially within Azure ecosystem.
Strong technical leadership skills, including mentoring and establishing engineering standards and best practices.
Capable of partnering with business and technical stakeholders to translate complex requirements into scalable data solutions.