Columbia Sportswear
Apparel and Footwear
PrincipalDataEngineer(DeliveryEngineering-DTC)
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“Principal Data Engineer (Delivery Engineering - DTC) at Columbia Sportswear. Skills: BI semantic modeling, Power BI, Data engineering, Analytics enablement. Own enterprise strategy and technical vision for BI. Design and govern shared, reusable, and conformed semantic”
Industry & Context.
Troubleshooting
What They're Looking For.
Must Have
Bachelor's degree in Computer Science, Information Systems, or related technical field, or equivalent practical experience, Proficiency in SQL for analytical workloads, Expert-level experience designing enterprise-scale Power BI semantic models, Deep mastery of dimensional modeling and star schemas, Deep mastery of complex DAX patterns and performance optimization, Working knowledge and experience with Azure data platforms, Experience integrating data from enterprise source systems
Nice to Have
Deeper experience with Databricks, Spark, or Delta Lake, Advanced DAX optimization and performance tuning, Exposure to data ingestion frameworks, orchestration, or cloud scale ELT patterns, Experience operating in a Global Capability Center (GCC) or distributed Agile team
What You'll Do.
Own enterprise strategy and technical vision for BI
Design and govern shared
and conformed semantic
Establish and enforce enterprise semantic modeling standards
Lead design reviews for high-impact semantic models
Define and maintain semantic model governance frameworks
Partner with Analytics Engineering and BI teams
Guide Power BI report developers
Champion enterprise metric consistency
Identify and mitigate systemic data quality or modeling
Provide technical leadership across the end-to-end analytics data
Partner with Data Architects to ensure Power BI
Influence ELT/ETL architecture decisions
Lead the design of curated analytical datasets and
Evaluate new Power BI and Azure analytics capabilities
Act as a technical mentor and coach
Contribute to Agile planning at a program or
Establish patterns that reduce long-term maintenance cost
Represent BI engineering in cross-functional forums
Influence Agile delivery planning
Proactively identify data issues
How You'll Work.
Team & Collaboration
Analytics Engineering teams; BI teams; Data Architects; Distributed teams; Cross-functional forums; Distributed Agile team
Process & Methodology
Agile planning, Agile delivery, Backlog refinement, Estimation, Sprint execution, Retrospectives
Full Job Description
Although we're an apparel and footwear-focused company, technology is central to everything we do. Columbia Sportswear’s Digital Technology (CDT) group enables an IT infrastructure across four global brands, a global supply chain, and 500+ geographically dispersed stores. These teams support in-store, mobile, and data platforms to enhance customer interface and service in an ever-evolving industry. The Principal Data Engineer – BI is a senior technical leader within Columbia Sportswear’s Global Capability Center (GCC) and Data & Analytics organization. This role is accountable for the architecture, governance, and long-term evolution of enterprise Power BI semantic models that power trusted analytics across Commercial, Supply Chain, and enterprise business domains. The Principal Data Engineer – BI operates at an enterprise scale—defining modeling standards, performance patterns, and best practices that are adopted across teams. This role serves as the technical authority for BI semantic modeling, ensuring that Power BI models are scalable, performant, intuitive, and aligned with upstream data engineering and downstream analytics needs. **HOW YOU’LL MAKE A DIFFERENCE** **Power BI Semantic Modeling (Primary Focus)** * Own the enterprise strategy and technical vision for BI semantic models, supporting Commercial, Supply Chain, and corporate analytics use cases. * Design and govern shared, reusable, and conformed semantic model layers that enable consistent analytics across domains and business functions. * Establish and enforce enterprise semantic modeling standards, encompassing dimensional modeling, DAX measures and calculation patterns, performance tuning and troubleshooting, and consistent design, naming, documentation, and reusability practices to ensure accurate, scalable, high‑performance self‑service analytics. * Lead design reviews for high‑impact semantic models, influencing BI architecture decisions and ensuring alignment with enterprise data and analytics s
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