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Mid-level Data Engineer title, metro location, and generalist Azure/SQL skillset increase applicant competition.
Data engineering skills are broadly transferable across industries despite Azure-specific tooling.
Explicit 3–4 years plus mandatory Azure ADF, SQL, and Power BI skills make filters strict.
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Design, develop, and maintain Azure Data Factory pipelines for data ingestion and transformation from REST APIs to Azure Blob Storage and Azure SQL Database.
Handle ad hoc data requests, troubleshoot production issues, and perform production support including monitoring and failure investigation of scheduled pipelines.
Collaborate with business users to create Power BI dashboards and reports; maintain code and deployments using Azure DevOps with version control and branching strategies.
3–4 years of experience as a Data Engineer or ETL Developer working with Microsoft Azure data integration technologies.
Strong hands-on experience with Azure Data Factory including pipelines, triggers, REST API integration, authentication, pagination, and incremental/historical data loads.
Proficient in SQL with experience in complex queries, stored procedures, views, window functions, and performance tuning.
Experience developing Power BI dashboards/reports connecting to Azure SQL and Azure Blob Storage data sources.
Independent operator able to own development tasks from ingestion pipeline through reporting and production support.
Experienced in end-to-end Azure cloud data integration and reporting solutions with strong troubleshooting and ad hoc request handling skills.
Familiar with version control and deployment in Azure DevOps, capable of collaborating with stakeholders to gather requirements and deliver business-focused data solutions.