Position Summary 

We are seeking a highly skilled Senior Data Engineer to design, develop, optimize, and support modern enterprise data platforms and data integration solutions. This role is responsible for building scalable data pipelines, data warehouses, ETL/ELT frameworks, and analytics-ready data architectures utilizing Microsoft Fabric, Azure Data Factory, Fabric Data Factory, SQL Server, and associated technologies. The ideal candidate will possess deep expertise in SQL development, data warehousing methodologies, ETL/ELT best practices, and cloud-based data engineering. Our engineering organization has adopted Specification-Driven Development (SDD) as a core practice emphasizing clear requirements, technical specifications, automation, quality, and maintainability. We also view Artificial Intelligence and AI-assisted engineering as strategic investments in our future. 

Key Responsibilities 

• Design, build, and maintain scalable, reliable, and secure data integration and analytics solutions. 

• Develop enterprise platforms utilizing Microsoft Fabric, Fabric Data Factory, Azure Data Factory, SQL Server, OneLake, Lakehouse, and Data Warehouse architectures. 

• Design and implement modern ETL and ELT frameworks supporting analytics, reporting, and operational use cases. 

• Develop ingestion, transformation, orchestration, monitoring, data quality, and reconciliation processes. 

• Participate in Specification-Driven Development practices and create technical specifications and design documentation. 

• Design solutions supporting AI, machine learning, generative AI, and advanced analytics initiatives. 

• Support CI/CD, monitoring, production operations, root-cause analysis, and continuous improvement. 

Required Qualifications 

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, or related field, or equivalent practical experience. 

• 7+ years of experience in data engineering, data warehousing, ETL/ELT development, or related disciplines. 

• Experience designing and implementing enterprise-scale data warehouse and analytics platforms. 

• Experience with Azure cloud-based data engineering solutions and Agile delivery practices. 

Required Technical Skills 

• Microsoft Fabric (Lakehouse, Data Warehouse, Data Factory, OneLake) 

• Azure Data Factory and Fabric Data Factory 

• SQL Server and advanced T-SQL 

• ETL and ELT architecture and development 

• Dimensional modeling and data warehouse design 

• Data orchestration and automation 

• Data governance and data quality 

• Git and Azure DevOps 

Preferred Qualifications 

• Pentaho Data Integration (PDI/Kettle) 

• SQL Server Integration Services (SSIS) 

• Azure Synapse Analytics 

• Azure Data Lake Storage 

• Spark and PySpark 

• Power BI 

• Data Vault methodology 

• Infrastructure as Code 

• CI/CD for data platforms 

• Experience with SDD methodologies 

• Experience supporting AI, machine learning, generative AI, or advanced analytics 

Legacy Platform Experience (Beneficial) 

• Qlik Replicate 

• Qlik Compose 

• Legacy ETL migration and modernization programs 

• CDC and data replication architectures 

• Migration of legacy data solutions into Microsoft Fabric and Azure environments 

Knowledge & Competencies 

• Kimball dimensional modeling 

• Fact and dimension design 

• Slowly Changing Dimensions (Types 1, 2, and 3) 

• Data quality and governance 

• CDC and incremental loading strategies 

• Metadata-driven processing 

• Performance optimization and scalability 

• Strong analytical, communication, and problem-solving skills 

Success Criteria 

• Build scalable, secure, and reliable enterprise data pipelines and integration solutions. 

• Deliver optimized Microsoft Fabric warehouse and lakehouse solutions. 

• Establish and follow ETL/ELT best practices and engineering standards. 

• Improve data quality, reliability, and accessibility. 

• Enable analytics, reporting, AI, and data-driven decision making. 

• Deliver high-quality solutions through disciplined Specification-Driven Development practices. 

• Actively contribute to the organization's AI strategy through architecture, automation, and innovation. 

• Help establish modern engineering practices that improve productivity, quality, and maintainability. 

• Work on a team leading in the adoption of Microsoft Fabric and AI-enabled data engineering capabilities.