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Mid-level Azure data engineer, metro locations and common title but platform specialization moderates applicant density.
Skills broadly transferable across industries but heavy Azure/Microsoft stack creates platform specificity.
Explicit 3-8 years requirement and mandatory Azure/MSBI stack increases filtering rigor.
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Own the full software development lifecycle (SDLC) of Azure Data Platform applications within a DevOps and Agile environment.
Design, develop, test, implement, and support scalable, secure, and resilient data engineering solutions on Microsoft Azure Data Platform.
Lead development of end-to-end Azure data analytics solutions including building reusable frameworks and mentoring peers on platform skills.
3-8 years of experience in MSBI with at least 3 years of hands-on experience in Azure Data Platform.
Bachelor's degree in Engineering, Math, Statistics, Econometrics, or a related discipline.
Proficient in Azure Data technologies including T-SQL, SSIS, SSAS, SSRS, Azure Data Factory, Azure Data Lake Store, Azure SQL DB and DW, Azure Analysis Services, Azure Data Bricks with Python/Scala, and Power BI.
Experience with Microsoft Fabric components such as Pipelines, Dataflows Gen2, ADLS Gen2, Notebooks, Lakehouse, Warehouse, and Delta tables.
Experienced in building scalable, optimized, and performance-tuned data pipelines using Microsoft Azure and Fabric technologies with a strong focus on SQL and PySpark.
Comfortable working end-to-end in Azure Data Platform with a DevOps (CI/CD) approach and capable of developing reusable framework solutions.
Able to follow best engineering practices for incremental versus full load data mechanisms and bridge technical execution with business logic understanding.