Principal Commercial Data Modeler

J  o  b    D  e  s  c  r  i  p  t  i  o  n

Job Profile Title:

Principal Commercial Data Modeler

Job Code:

12788

Business_Title

Principal Commercial Data Modeler

Profile Title:

12788 Principal Commercial Data Modeler

Grade / Band:

IC5

FLSA Status:

Exempt

The Principal Commercial Data Modeler is a recognized expert responsible for the vision and strategy of enterprise data models and metrics, with a strong focus on SQL and PySpark implementations in cloud environments. This role defines how decision-ready aggregates are structured, consumed, and trusted across the enterprise. The Principal Data Modeler partners with Data Engineering, Marketing, Revenue Management, Data Science, and Analytics to shape enterprise data strategy. The role combines strategic oversight with hands-on guidance, ensuring high-quality, scalable, and consistent business metrics.

Define and own the enterprise data modeling vision and multi-year roadmap, spanning conceptual, logical, and physical models across customer, transaction, product, marketing, and operational domains.

Establish and govern enterprise standards for metrics, aggregates, and KPIs, including grain definitions, calculation logic, attribution rules, and time-based semantics to ensure consistency across reporting, experimentation, and AI use cases.

Lead cross-domain semantic design initiatives, aligning data models across Marketing, Loyalty, Digital, Revenue Management, and Operations to enable interoperable analytics and shared business definitions.

Serve as a strategic advisor to executive leadership, translating modeling and semantic decisions into business impact on revenue optimization, personalization, measurement accuracy, and decision velocity.

Influence cloud data platform and architecture strategy, including lakehouse modeling patterns, medallion layers, semantic layers, feature stores, and integration with BI, activation, and ML platforms.

Design and implement governance frameworks for metric reliability and trust, including certification workflows, lineage, versioning, ownership models, and change management for enterprise KPIs.

Ensure data models are optimized for analytics, AI/ML, and operational consumption, supporting use cases such as personalization, experimentation, forecasting, fraud detection, and real-time decisioning.

Guide and review SQL and PySpark implementations in cloud environments, ensuring performant, maintainable, and scalable modeling patterns for batch and streaming workloads.

Drive strategic initiatives that connect analytics to activation, enabling consistent metrics and features across dashboards, marketing platforms, personalization engines, and downstream applications.

Own and evolve enterprise-level semantic layers and curated datasets, balancing flexibility for advanced analytics with guardrails that protect metric integrity.

Champion adoption of trusted, decision-ready metrics across domains, partnering with Analytics, Data Science, and Business teams to reduce metric fragmentation and improve confidence in insights.

Performs other job-related duties as requested

Proof of eligibility to work in the United States

Bachelor's Degree

Computer Science, Information Technology, or related field

Required

10+ Years of Prior Relevant Experience

Data modeling, analytics, or related field with enterprise-level impact

Required

Organization-wide influence on data modeling and analytics strategy.

Required

Drives adoption of trusted, decision-ready metrics across all domains.

Required

Shapes enterprise-level standards, semantic layers, and governance.

Required

Familiarity with dbt, Unity Catalog, or similar modeling frameworks.

Preferred

Expert SQL & PySpark proficiency with the ability to design, optimize, and troubleshoot enterprise-scale pipelines, queries, and transformations.

Enterprise-Level Modeling expertise with mastery of dimensional, relational, event-based, and canonical models; multi-domain integration; complex hierarchy and aggregation logic.

Semantic Layer Leadership ability to define and enforce enterprise-wide semantic layers, metric definitions, and aggregation standards.

Cloud Data Platform Mastery advanced knowledge of Databricks, Delta Lake, Unity Catalog, and large-scale cloud optimization techniques.

Design and enforce enterprise standards for validation, anomaly detection, completeness, drift monitoring, and lineage tracking.

Performance Optimization with the ability to lead advanced strategies for partitioning, caching, query/pipeline tuning, and large-scale transformation performance.

Establish governance frameworks, version control, deployment automation, and enterprise pipeline best practices.

Enterprise Documentation & Metadata Strategy with lead standardization of metadata, lineage, and model documentation for cross-functional adoption.

Balancing

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Carrying 10 pounds

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Clear speech - simple

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Clear speech - complex

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Climbing

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Distant vision

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Driving

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Flexibility - upper body

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Flexibility - lower body

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Hearing/Listening

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Kneeling

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Lifting 10 pounds

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Near vision

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Normal vision

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Pushing/Pulling

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Reaching

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Sitting

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Standing

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Typing

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Walking

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Communication - verbal

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Communication - written

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Confined area

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Contacts - works alone

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Contacts - works around others

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Contacts - works with others

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Exposure to dust / dirt

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Exposure to fumes / odors

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Extreme cold

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Extreme heat

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Fast pace

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Hazardous conditions - chemicals

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Hazardous conditions - high structures

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Hazardous conditions - high voltage

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Indoors

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Noise levels - low to moderate

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Noise levels - high

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Office conditions

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Outdoors

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Restricted area

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Travel

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Analytical

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Clerical

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Comprehension

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Crisis incidents

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Customer service

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Decision making

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High pressure

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Judgment

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Long hours

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Math skills - advance

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Math skills - basic

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Organization

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Reading - simple

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Reading - complex

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Repetition

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Tight deadlines

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Writing - simple

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Writing - complex

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