Data Product Owner (Remote, GCP, BigQuery, E-commerce, LATAM) #26400

• Initial duration: 3 to 6 months, with strong possibility of extension.
• Must be able to work in the Pacific Time Zone.
Stack: GCP, Google BigQuery, Snowflake, Redshift

• 7+ years of experience in data analytics, data products, business intelligence, data warehousing, or related roles.

• Strong hands-on experience translating business requirements into technical data requirements.

• Significant direct experience in fashion, apparel, specialty retail, or a closely related omnichannel retail environment is strongly preferred and will be a major selection criterion.

• Deep understanding of retail terminology, processes, data structures and KPIs.

• Experience designing or defining dimensional data models and analytical data marts.

• Advanced SQL skills and ability to independently interrogate large datasets.

• Experience with modern cloud data warehouses or data lakes such as Google BigQuery, Snowflake, Databricks or Redshift.

• Experience working closely with Data Engineers on transformation logic and curated datasets.

• Strong understanding of fact/dimension models, star schemas, slowly changing dimensions, data grain, master/reference data, data lineage, data quality, business metadata and data contracts.

Business-to-Data Product Ownership

• Partner directly with business leaders and SMEs across Merchandising, Planning & Allocation, Buying, Inventory, Store Operations, Ecommerce, Supply Chain, Finance, Marketing, Customer and Loyalty.

• Lead discovery sessions to understand business processes, decisions, KPIs, reporting requirements, analytical use cases and data pain points.

• Translate business needs into detailed technical requirements for Data Engineers.

• Create clear data-product requirements, user stories, acceptance criteria, source-to-target mappings, transformation rules and metric definitions.

• Own and prioritize the requirements backlog for business-facing data products and data marts.

Retail Data Model & Data Mart Design

• Work with Data Engineering and business SMEs to define reusable, trusted data models for sales, demand, inventory, receipts, purchase orders, allocation, replenishment, sell-through, weeks of supply, inventory turns, gross margin, markdowns, AUR, UPT, store performance, ecommerce, returns, fulfillment, customer behavior, loyalty and financial metrics.

• Define product and organizational hierarchies including department, class, subclass, style, color, size, SKU, store, region, channel and other key business dimensions.

• Ensure common metrics have one governed enterprise definition regardless of whether they are consumed through dashboards, SQL, AI applications or machine-learning models.

Semantic Layer & AI Readiness

• Help design Windsor's enterprise semantic/metrics layer on top of BigQuery.

• Define business-friendly entities, dimensions, measures, relationships, hierarchies and terminology.

• Establish consistent definitions for frequently used retail KPIs.

• Work with Data Engineering and AI teams to ensure data products can be safely consumed by BI, self-service analytics, conversational analytics, AI agents, machine-learning models, forecasting and optimization applications.

• Help establish metadata and business context so AI tools understand what the data represents rather than relying only on physical table and column names.

Data Quality & Governance

• Define business validation rules and expected data-quality thresholds.

• Partner with Data Engineering to validate new data marts before production release.

• Reconcile data products against existing operational and financial systems.

• Identify authoritative systems of record for business metrics.

• Document data lineage, ownership, definitions and business rules, and help establish a business glossary and enterprise KPI catalog.