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

Evolvesquads inc.; United States; Remote; USD 130,000 - 190,000; Full time

Job summary

Evolvesquads inc. is seeking a Senior Data Product Owner to bridge business needs with Data Engineering for an enterprise data platform on Google BigQuery. You will craft data-products, data marts, and semantic-layer requirements aligned with retail KPIs and governance.

This is a remote role with an initial 3–6 month engagement and Pacific Time availability. You will collaborate with Merchandising, Planning, Inventory, Ecommerce and Finance teams, and translate business requirements into

Qualifications

• 7+ years of experience in data analytics, data products, BI, 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 omnichannel retail environment.
• 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 interrogate large datasets.
• Experience with cloud data warehouses or data lakes (BigQuery, Snowflake, Databricks, Redshift).
• Experience working with Data Engineers on transformation logic and curated datasets.
• Understanding of fact/dimension models, star schemas, slowly changing dimensions, data lineage, quality and contracts.

Responsibilities

• Partner with business leaders across Merchandising, Planning & Allocation, Buying, Inventory, Store Ops, Ecommerce, Supply Chain, Finance, Marketing and Loyalty.
• Lead discovery sessions to understand processes, KPIs, reporting requirements, analytics use cases and data pain points.
• Translate business needs into technical requirements for Data Engineers.
• Create data-product requirements, user stories, acceptance criteria, mappings and metric definitions.
• Own and prioritize the backlog for business-facing data products and data marts.
• Define reusable, trusted data models for sales, demand, inventory and other metrics.
• Define product hierarchies including department, class, style, color, size, SKU, store, region, channel.
• Ensure common metrics have a single enterprise definition across dashboards, SQL, and AI apps.

Skills

7+ years in data analytics and related

Translate business requirements to 技n

Retail/fashion domain experience

Advanced SQL skills

Cloud data warehouses (BigQuery/Redsh

Collaboration with Data Engineers

Dimensional data modeling knowledge

Tools

Google BigQuery

Snowflake

Redshift

Databricks

Job description

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

... create world-changing products using God-given talents . .

PROJECT DESCRIPTION:

We are building an enterprise data platform on Google BigQuery to create a trusted foundation for analytics, reporting, machine learning, and AI.

We are seeking an experienced Senior Data Product Owner who will serve as the bridge between our company's business functions and Data Engineering team. Data Engineers are focused on building ingestion pipelines and bringing source-system data into the Bronze layer. This role owns the critical next step: understanding how the business uses the data and translating those needs into data marts, business metrics, dimensional models, data products, and semantic-layer requirements that Data Engineering can implement.

PROJECT STACK and TEAM:

• 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

MAIN REQUIREMENTS:

• 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.

GOOD TO HAVE:

• Google BigQuery and Google Cloud Platform.
• Looker / LookML or another semantic-layer or metrics-layer technology.
• Enterprise metrics-layer development, data catalogs and business glossaries.
• AI-ready enterprise data architectures and conversational analytics.
• Retail merchandising, POS, OMS, WMS, ecommerce, CRM/CDP and Planning & Allocation platforms.
• Experience with platforms such as Shopify, Manhattan, Aptos, NewStore, NetSuite, or comparable retail systems.

JOB RESPONSIBILITIES:

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.
• 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.

SUMMARY:

Work your way– Enjoy the freedom to work from anywhere, with flexible hours that match your natural rhythm.

Work with global clients– Collaborate directly with international teams to create real impact.

Great people, no micromanagement– Join a supportive, results-focused team where you’re trusted to do your best work.

This flexibility allows developers…

A better work-life balance

Increased productivity

The ability to work any time around the clock