Solution Architect – Data

Role Overview

• Lead business stakeholder workshops to surface data needs, refine use cases, and drive ambiguous asks toward precise, answerable questions
• Partner with Product to challenge, sharpen, and validate requirements before engineering investment begins
• Translate business outcomes — loyalty, customer lifetime value, athlete behavior, in-store and digital performance — into data requirements and analytical framings
• Analyze large, complex datasets across customer, transactional, behavioral, and operational domains to validate requirements, identify patterns, and inform solution design
• Develop and socialize analytical findings that directly influence product and platform decisions
• Support self-service data enablement by helping business users understand and interact with data assets more effectively
• Produce clear, professional artifacts including current and future state data flows, domain data models, source-to-target mappings, data dictionaries, and decision records
• Document data lineage, governance considerations, and integration patterns in a way that is accessible to both technical and non-technical audiences
• Apply data architecture principles to evaluate, design, and recommend solutions across our cloud data platforms
• Contribute to enterprise data model standards, integration patterns, and platform decisions in partnership with engineering and foundational tech teams
• Assess data quality, lineage, and governance implications of proposed solutions
• Ensure designs account for scalability, reliability, and cost — without over-engineering for the problem at hand

Requirements

• 7–10 years of experience spanning data engineering, analytics, and/or solution architecture
• Demonstrated ability to lead discovery sessions and translate business problems into data requirements — asking 'what question are you trying to answer?' before reaching for a tool
• Strong hands-on SQL and Python skills; you are comfortable getting into the data yourself
• Experience with customer and loyalty data in a retail or omnichannel commerce context
• Comfort working across behavioral, transactional, and operational datasets at enterprise scale
• Excellent written and verbal communication — creating artifacts and documentation that are clear, practical, and widely adopted by teams
• Experience with cloud-based data platforms and modern data architecture patterns (Medallion architecture, Data Mesh concepts, Data Catalog, Data Quality frameworks)
• Ability to hold a room: facilitating workshops, presenting findings, and influencing without authority

Tech Stack

• Cloud
• Python
• SQL

Benefits

• incentive
• equity
• benefits