Senior Data Modeler

WHO YOU'LL WORK WITH

MAC BI is the business intelligence function for Nike's Marketing, Activity, and Converse organizations. We own the data pipelines, reporting platforms, and AI-powered insights that inform how Nike spends its marketing budget, understands its competitive position, listens to its consumers, and coaches its athletes. We're a team of engineers, analysts, and data scientists distributed across Beaverton and Bangalore, operating under Marketing Technology & Converse.

This role will report to the Director of Marketing BI.

WHO WE ARE LOOKING FOR

Nike is transforming its digital activity platforms (Nike Run Club, Nike Training Club) into AI-powered coaching experiences - and MAC BI is building the data and intelligence foundation that makes it possible. This role designs the data models that make it all work.

You'll own the logical and physical data architecture for the Activity domain - defining how wearable device streams from Garmin, Apple Watch, and COROS become a unified athlete profile, how structured training inputs (goals, baselines, injury history) merge with unstructured coaching data (notes, feedback, RPE), and how Nike Sport Research Lab's sport science models connect to production coaching recommendations. The models you design will be the contract between raw device data and the AI Coaching Engine that serves every athlete on the platform.

Must Have

• Bachelor’s degree in computer science, data science, data analytics, or a related field. Will accept any suitable combination of education, experience or training
• 5+ years of data modeling experience across analytical and operational data systems, with significant experience designing models for large-scale data platforms (Databricks, Snowflake, BigQuery, or equivalent)
• Deep expertise in dimensional modeling, Data Vault, or medallion architectures - you have strong opinions on when to use star schemas vs. wide tables vs. normalized structures, and you can justify those opinions with performance and usability tradeoffs
• Production experience designing models for multi-source data harmonization - you've solved the problem of taking data from multiple vendors/systems with different schemas and creating a unified, consistent representation that downstream consumers can trust
• Advanced SQL and data profiling skills - you can explore a new source system, understand its structure, identify quality issues, and design a target model in the same sitting
• Experience producing Source-to-Target Mappings - you've documented complete transformation paths from source to consumption layer and worked with engineers to implement them
• Strong collaboration skills with both engineering and analytics - your models need to be buildable by engineers and usable by analysts; you can speak both languages

Strong Preference

• Experience with IoT, wearable, or sensor data modeling - time-series schemas for device streams, handling irregular sampling rates, modeling device-specific metadata alongside measurement data
• Experience with health, fitness, or sport science data - athlete profiles, training load constructs, physiological measurements, or coaching/training plan structures
• Familiarity with Databricks/Spark physical design considerations - Delta Lake partitioning strategies, Z-ordering, liquid clustering, table optimization for both batch and interactive query patterns
• Experience with schema evolution and versioning strategies - managing model changes in production without breaking downstream consumers
• Comfort with async collaboration across time zones - clear documentation, self-explanatory model diagrams, and proactive communication

Nice to Have

• Familiarity with Nike's internal data platforms (Sole, Databricks on Nike infra)
• Personal background in endurance sports, coaching, or sport science (you understand the domain because you've lived it)
• Experience with data privacy modeling for health/fitness data (PII handling, consent-based access patterns)
• Experience modeling for ML/AI consumption - feature stores, training datasets, or model input/output schemas

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