Full Time
10+
40
Jun 9, 2026
Senior Data Engineer (Data Engineering, Data Warehousing & Analytics)
Position Summary
The IT Data Engineer is responsible for designing, building, maintaining, and modernizing the organization's data platform and reporting ecosystem. This role serves as the primary technical owner of data engineering initiatives, working closely with executive leadership, business stakeholders, and development teams to transform operational and third-party data into trusted, actionable business insights.
The position combines data engineering, data warehousing, reporting, and business partnership responsibilities. The successful candidate will help drive the organization's transition toward a modern, data-driven culture while supporting both current and future analytics initiatives.
Key Responsibilities
• Design, develop, and maintain data pipelines integrating internal and external data sources.
• Build scalable ETL/ELT processes and data quality controls.
• Maintain and enhance dimensional models, fact tables, and dimension tables.
• Create mapping tables and conformed dimensions for cross-system reporting.
• Develop and support Power BI dashboards, reports, and datasets.
• Partner with business stakeholders to gather requirements and deliver analytics solutions.
• Support migration from SQL Server-based warehousing to cloud-based platforms.
• Monitor and optimize reporting, query, and warehouse performance.
• Serve as a trusted advisor on data strategy and analytics initiatives.
Initial Priorities (First 6–12 Months)
• Learn business operations, products, inventory, and distribution processes.
• Integrate new third-party datasets into the data warehouse.
• Expand Power BI reporting and analytics capabilities.
• Improve data quality and standardization across systems.
• Develop a roadmap for cloud-based modernization and future data architecture.
Technical Environment
Current: SQL Server, Power BI, Star Schema Modeling, ETL Processes, Operational Systems.
Future: Microsoft Fabric, Azure Data Lake, Cloud Data Platforms, Modern Data Warehousing Architectures, Advanced Analytics Solutions.
Success Measures
• Strong understanding of business operations and stakeholder needs.
• Successful integration of new data sources.
• Increased reporting adoption and business value.
• Improved data quality and trust in reporting.
• Progress toward modernization and cloud adoption.
• Establishment of scalable data engineering practices.
(Databricks is a plus)
Overall Mission
Build and evolve the organization's data platform, integrate new data sources, improve analytics and reporting capabilities, and help transform the company into a data-driven organization through modern data engineering and business intelligence solutions.