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Mid-level generalist Data Engineer in a metro location with common skills increases candidate competition.
Azure-specific tooling and EDW migration focus moderately limit cross-industry transferability.
Multiple explicit years, mandatory Azure data services and data-quality skills make shortlisting strict.
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Own design and implementation of automated processes to measure data quality, transmission failures, and load errors in ETL data pipes during EDW migration to Azure Cloud.
Collaborate with data pipe builders, business analysts, and report developers to define data quality requirements and create dashboards/reports showcasing these metrics.
Analyze and improve existing data systems, including data modeling tasks aligned with Kimball dimensional modeling, and support CI/CD framework tuning in Azure DevOps.
Bachelor's Degree or higher in Engineering, Computer Science, or a related technical field.
At least 5 years of experience designing, building, and supporting data pipelines with minimum 2 years creating automated data quality measurement solutions for ETL processes.
Minimum 1 year recent experience with Azure Data Services including Azure Data Catalog, Blob Storage, Data Lake Storage, Synapse Analytics, Analysis Services, and Data Factory.
Strong Microsoft SQL Server (preferably 2016) experience and familiarity with PowerShell and Python scripting, plus experience with Azure DevOps CI/CD processes.
Experienced data engineer specialized in data quality assurance during cloud migration projects, particularly with Azure environments.
Technically proficient in integrating data quality monitoring into ETL pipelines and skilled at collaboration across stakeholder, analyst, and developer teams.
Comfortable working with dimensional data modeling (Kimball methodology) and able to improve data reliability and remediation processes in complex enterprise data systems.