Principal Data Engineer (Delivery Engineering - DTC)
Job Description
POSITION SUMMARY
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 is responsible for the design, development, testing, maintenance, and supporting of CSC's enterprise data assets. Dedicated to a single offshore scrum team within our Global Capability Center (GCC), this role serves as a technical authority who drives high-quality engineering practice across the analytic environment for CSC's D&A program. The Principal Data Engineer enables improvements in environment design, efficiency, controls, and the overall user experience of enterprise data assets at CSC. This position leads activities including dimensional modeling, approaches, SQL script creation, stored procedure development, data lake and databricks development, integration services support, and DevOps aligned practices to enhance reliability, efficiency, and overall user experience of enterprise data solutions. The Principal Data Engineer coordinates with senior leadership, several enterprise support organizations and CSC end users.
HOW YOU’LL MAKE A DIFFERENCE
Continuously deliver, enhance, and optimize high-priority enterprise data assets that power Columbia’s global analytics, reporting and visualization capabilities, ensuring solutions are scalable, reliable, and aligned with modern engineering standards.
Design, build, test, and maintain end-to-end data management solutions across Columbia’s global data lake and databricks environment, applying strong solution design principles, reusable design patterns, and industry-standard data modeling techniques such as Kimball, Inmon, and relational/OLTP approaches.
Identify and communicate project risks and technical impediments early, partnering with the Manager and cross-functional team members to ensure timely resolution and successful delivery within defined scope, timelines, and expectations.
Apply Agile ways of working to iteratively refine requirements, define acceptance criteria, plan development activities, and deliver high-quality enterprise data solutions through continuous collaboration and incremental value delivery.
Serve as a technical representative for Data Engineering within assigned initiatives, collaborating closely with architecture partners to evaluate solution options, recommend design patterns, and ensure alignment with enterprise architecture principles.
Solve complex technical problems that span multiple systems or projects, driving improvements in engineering practices, data quality, performance and maintainability. Provide mentorship, technical guidance, and best-practice leadership to elevate engineering capability across the team.
YOU ARE
A professional with sound judgment and decision-making skills
A self-starter with an appropriate sense of urgency
YOU HAVE
Bachelor’s Degree requires Computer Science, Information Systems or a related field or an equivalent combination of education and experience may be considered.
8+ years of hands-on experience engineering, architecting, developing and optimizing large-scale data and analytics solutions, including BI, data warehousing, or enterprise data platforms.
Expert-level proficiency in SQL development and query optimization for data platforms such as Azure SQL DW, Databricks, SAP BW/HANA, Oracle, or equivalent technologies. Skilled in building optimized semantic models and Analysis Services/ Power BI semantic models.
Deep understanding of database and data modeling concepts, including normalization, indexing, physical and logical modeling, performance tuning, and large-scale relational design.
Strong knowledge of dimensional modeling and enterprise data warehousing techniques, including Kimball, Inmon, and related practices.
Hands-on experience implementing and maintaining star schemas, along with expertise in slow changing dimensions (SCDs), late arriving data, and other warehousing patterns.
4+ years’ professional experience building and supporting ETL/ELT solutions using tools such as Data Factory, Databricks, Informatica, SQL Server Integration Services, Business Objects Data Services, or IBM Datastage.
4+ years’ experience developing and supporting data visualization and analytical solutions, with strong proficiency in Microsoft Power BI, including semantic modeling and DAX.
Familiarity with data domains common to Commercial, and Supply Chain environments, or experience integrating data sourced from SAP ERP or Microsoft Dynamics or similar large-scale enterprise systems.
Experience working with large-scale or distributed data processing technologies, optimizing data structures, and designing scalable data pipelines (experience with Azure cloud ecosystem strongly preferred).
Hands-on experience with Microsoft Azure data services, such as Azure Data Factory, data Lake, Databricks, Synapse, or other modern cloud data platforms.
Advanced DAX skills and proficiency in languages such as Python, PowerShell, R, or Java to support automation, scripting, or advanced analytics.
Bonus: experience with AI/ML engineering, automation agents, generative AI capabilities, or MLOps practices
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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.