Analytics Engineer designing Power BI semantic models and scalable data architecture. Building real-time analytics infrastructure for Krux’s mining-industry SaaS products.
Responsibilities
Design, develop, and maintain enterprise-scale Power BI semantic models (tabular)
Architect scalable and high-performance data models for multi-tenant SaaS analytics
Define best practices for dataset design, governance, and lifecycle management
Optimize model performance, DAX calculations, aggregations, and storage strategies
Manage and enhance Power BI service environments, including workspaces, deployment pipelines, and security
Monitor and improve analytical database performance in cloud environments
Conduct model tuning and capacity optimization to support growth and usage scaling
Evaluate and enhance the current analytics platform architecture
Partner with teammates on ETL and data pipeline improvements
Provide technical guidance on semantic layer design and analytics best practices
Support cross-functional stakeholders with scalable reporting architecture
Assist with high-priority analytics platform issues
Report to the Software Development Manager
Requirements
Bachelor’s Degree (Computer Science, Technology, Engineering, or related field)
10+ years of experience as a data analytics developer or engineer
5+ years of experience developing data architecture solutions using Tabular Editor is highly desirable
Experience in the energy, drilling and/or mining sector is an asset
Experience with databases for high performance, multi-tenant web applications is necessary
Familiarity with Azure cloud data services
Experience with development tools including Git, Azure DevOps, Visual Studio, and Jira
Understanding of modern data development technologies, including data engineering, pipeline, and ETL
Expert in Power BI data modeling and Power BI services
Strong attention to detail, critical thinking, and problem-solving skills
Benefits
Hybrid work schedule
Flexible hours
Work-life balance
Mentorship and professional development through continuous learning
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