Senior Data Engineer - Information Technology (Kansas City)

Position Summary

We are seeking an accomplished Senior Data Engineer with 8 years of hands-on experience to join our data team. This role combines deep expertise in data pipeline engineering ETL- SQL Server, Informatica, Active Batch, and SSAS - with strong semantic modeling capabilities, including Power BI and dimensional data modeling. The ideal candidate is equally comfortable supporting and enhancing production pipelines and supporting high-performance analytical models that drive business intelligence across the enterprise.

Key Responsibilities 

Pipeline Engineering & Production Support

• Monitor, support, and enhance existing data pipelines built on SQL Server, Informatica, and Active Batch, ensuring high availability and data integrity.
• Develop, maintain, and optimize ETL/ELT processes; troubleshoot performance bottlenecks and data quality issues with minimal downtime.
• Manage and tune Active Batch job workflows for reliable, efficient automation.
• Collaborate with cross-functional teams to resolve data incidents, communicate root cause, and implement preventive measures.

• Document pipeline processes, data flows, and technical specifications to support operational continuity and knowledge transfer.

   

Data Modeling & Semantic Layer

• Design, develop, and optimize SSAS tabular models and lead migration efforts to Power BI semantic models.
• Build and maintain Power BI semantic models leveraging data from SQL Server; ensure high-performance query execution.
• Write, optimize, and troubleshoot SQL, DAX, and Power Query (M) to support analytics and reporting needs.
• Partner with data engineers to design tables and schemas for optimal analytical performance.
• Document data models, business rules, and data mappings using catalog tooling.
• Ensure smooth daily operations of deployed data models; address outages promptly.

Stakeholder Engagement & Mentorship

• Collaborate with business and technical stakeholders to gather requirements and deliver data products aligned to business needs.
• Train and mentor junior engineers and modelers on best practices for pipeline development, data modeling, and optimization.
• Research and recommend tools, methods, and technologies to continuously improve data engineering and modeling practices.