Sr. Data Engineer (Delivery Engineering - DTC)

About the role

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 Senior Data Engineer - Power BI is a key contributor within Columbia Sportswear's Global Capability Center (GCC) and Data & Analytics team. This role is primarily focused on designing, building, and supporting Power BI semantic models that enable trusted, high-performance analytics across Commercial, Supply Chain and enterprise business domains. While the role's core responsibility is Power BI data modeling, the position is intentionally designed to grow into a full stack Data Engineer. Over time, this engineer will deepen hands-on experience across Azure Databricks, data ingestion pipelines, and the enterprise Data Lake, partnering closely with senior engineers and architects to deliver end-to-end data solutions. This role is ideal for an engineer who enjoys working closely with analytics consumers, cares deeply about data model quality and usability, and wants to expand into modern cloud data engineering. HOW YOU'LL MAKE A DIFFERENCE Power BI Semantic Modeling (Primary Focus) Design, develop, and maintain Power BI semantic models that support Commercial , Supply Chain and enterprise analytics use cases Apply strong dimensional modeling principles (facts, dimensions, conformed dimensions) to enable intuitive, high performing ‑self-service‑ analytics Develop and optimize DAX measures, calculations, and model logic to ensure accuracy, scalability, and performance Own semantic model troubleshooting, performance tuning, and enhancement requests in partnership with analytics and business stakeholders Establish and follow best practices for model design, naming standards, measure governance, and reusability Analytics Enablement & Collaboration Partner closely with analysts and product teams to translate analytical requirements into well-structured‑ Power BI models Advise Power BI report developers on model usage, performance considerations, and platform capabilities (without building reports) Support data quality investigations and root cause analysis across semantic, warehouse, and source data layers Data Engineering (Growth Path) Build and support ELT/ETL pipelines that feed Power BI semantic models using Azure Databricks, Azure Data Factory, and the Enterprise Data Lake Participate in the design and delivery of curated analytical datasets and star schemas optimized for BI workloads Learn and apply Databricks and Spark-based data transformations, expanding capability as a full stack‑ data engineer Contribute to scalable data engineering patterns while guided by senior engineers and architectural standards Delivery & Ways of Working Participate fully in Agile delivery processes, including backlog refinement, estimation, sprint execution, and retrospectives Proactively identify data issues, technical risks, and improvement opportunities, escalating appropriately Collaborate effectively within a global, distributed engineering team YOU ARE Passionate about Power BI data modeling and analytical usability Comfortable working close to the business while maintaining engineering rigor Curious and motivated to grow beyond BI into modern data engineering A collaborative team member who values quality, clarity, and continuous improvement YOU HAVE Bachelor's degree in Computer Science, Information Systems, or a related technical field, or equivalent practical experience Strong proficiency in SQL for analytical workloads, including query optimization and data validation Hands on experience building and supporting Power BI semantic models, including: Star schemas and dimensional models Measures, calculated columns, and KPIs Basic to intermediate DAX Solid understanding of data warehousing and dimensional modeling concepts, including SCDs and common BI patterns Experience supporting self-service analytics and resolving semantic or data model related issues Familiarity working with Azure data platforms, such as Azure Data Lake, Azure Databricks, Azure Data Factory, or similar tools Experience integrating data from enterprise source systems (e.g., DTC, Supply Chain, ERP, SAP, or similar domains) Nice to Have / Growth Areas 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 #LI-SA1 #Hybrid This job description is not meant to be an all-inclusive list of duties and responsibilities, but constitutes a general definition of the position's scope and function in the company.