<p>We are seeking a Senior Data Engineer to maintain legacy ETL processes and lead the development of modern, cloud-native data pipelines on Microsoft Azure. This role involves working closely with stakeholders to design, implement, and optimize data solutions for analytics, reporting, and AI/ML initiatives.</p><p><br></p><p>Key Responsibilities:</p><ul><li>Maintain and operate legacy ETL processes using Microsoft SSIS, PowerShell, SQL procedures, SSAS, .NET code</li><li>Design and implement Azure cloud-native data pipelines using Azure Data Factory, Synapse Pipelines, Apache Spark Notebooks, Python, and SQL</li><li>Redevelop existing SSIS ETL scripts into Azure Data Factory and Synapse Pipelines</li><li>Support data modeling for relational, dimensional, data lakehouse (medallion architecture), data warehouse, and NoSQL environments</li><li>Implement data migration, integrity, quality, metadata management, and security measures</li><li>Monitor and troubleshoot data pipelines to ensure high availability and performance</li><li>Automate platform operations with governance, build, deployment, and monitoring processes</li><li>Actively participate in Agile DevOps practices, including PI planning</li><li>Maintain strict versioning and configuration control for data integrity</li></ul>

<p>Required Qualifications:</p><ul><li>BS in Computer Science or related field with 8+ years of experience OR Master’s with 6+ years</li><li>4+ years of experience developing and maintaining ETL processes using SSIS, SQL, SQL Agent, PowerShell</li><li>4+ years of experience with scripting languages: SQL, T-SQL, Python, PySpark</li><li>Experience with Microsoft BI tools: SQL Server, SSIS, SSRS, SSAS (cubes)</li><li>Proven experience designing and building ETL/data engineering solutions using Azure Data Lake Services, Azure Synapse</li></ul><p>•Preferred Skills:</p><ul><li>Experience with data lakehouse architectures and medallion design</li><li>Familiarity with AI/ML data preparation and advanced analytics workflows</li><li>Strong understanding of data governance and security best practices</li></ul>

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