Senior Data Modeler - MAC BI, Activity Intelligence

About the Role The role sits within MAC BI, the business intelligence function for Nike's Marketing, Activity, and Converse organisations, and reports to the Director of Marketing BI. It owns the logical and physical data architecture for the Activity domain, designing data models that turn wearable device streams and coaching inputs into a unified athlete profile. The models serve as the contract between raw device data and the AI Coaching Engine that powers Nike's digital activity platforms. The role works closely with engineers, analysts, and data scientists to build the data and intelligence foundation for AI-powered coaching experiences. Key Responsibilities Design the unified athlete profile data model that harmonises wearable device streams (heart rate, pace, cadence, elevation, sleep, HRV, VO2max, body battery/recovery) from multiple vendors into a single, query-optimised representation on Databricks/Sole. Model the coaching intelligence data layer, defining how structured inputs (intake questionnaires, training goals, fitness baselines, injury/pain history) and unstructured inputs (coach notes, athlete feedback, session RPE) are organised, linked And exposed for the AI Coaching Engine and coaching dashboards. Design the NSRL-to-athlete data model that translates Nike Sport Research Lab sport science constructs (Horwill's predictors, Daniels VDOT, Acute Chronic Workload Ratio, training periodisation models) into production-ready data structures for coaching algorithms at scale. Own the Bronze/Silver/Gold medallion layer design for the Activity domain, defining transformation contracts, grain, and business rules at each layer within the MAC BI standardised pipeline template. Produce Source-to-Target Mappings for all Activity data sources, documenting the complete transformation path from raw device vendor schemas through to Gold-layer consumption models. Handle multi-vendor device data modelling complexity by resolving schema conflicts, mapping equivalent metrics across devices (e.g., Garmin Body Battery vs. Apple Watch recovery estimates vs. COROS stamina), and defining rules for when sources overlap or conflict. Design data contracts between the Activity data foundation and downstream consumers, defining API schemas, refresh cadences, SLAs, and versioning strategy for NRC/NTC product teams, the AI Coaching Engine, and coaching staff tools. Model race and sport experience data, including training plan structures, race-specific schemas (5K through ultra), in-race coaching signal formats, and post-race performance analysis structures across NRC marathon programmes. Define data quality rules at the model level by embedding Spark Expectations checks, referential integrity constraints, and business rule validations into the model design, and establish the Activity data dictionary and catalogue as a living source of authoritative definitions. Own the schema evolution strategy for new device vendors, sensor types, sport science models, and coaching features without breaking existing consumers, and partner with data analysts to validate that model designs support analytical use cases efficiently. Required Experience & Qualifications Bachelor's degree in computer science, data science, data analytics, or a related field; any suitable combination of education, experience, or training will be accepted. 5+ years of data modelling experience across analytical and operational data systems, with significant experience designing models for large-scale data platforms such as Databricks, Snowflake, BigQuery, or equivalent. Deep expertise in dimensional modelling, Data Vault, or medallion architectures, with strong opinions on when to use star schemas versus wide tables versus normalised structures and the ability to justify those opinions with performance and usability trade-offs. Production experience designing models for multi-source data harmonisation, including solving the problem of taking data from multiple vendors or systems with different schemas and creating a unified, consistent representation that downstream consumers can trust. Advanced SQL and data profiling skills, including the ability to 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, including documenting complete transformation paths from source to consumption layer and working with engineers to implement them. Strong collaboration skills with both engineering and analytics, with the ability to create models that are buildable by engineers and usable by analysts. Preferred Skills Experience with IoT, wearable, or sensor data modelling, including time-series schemas for device streams, handling irregular sampling rates, and modelling device-specific metadata alongside measurement data. Experience with health, fitness, or sport science data, such as athlete profiles, training load constructs, physiological measurements, or coaching/training plan structures. Familiarity with Databricks/Spark physical design considerations, including Delta Lake partitioning strategies, Z-ordering, liquid clustering, and table optimisation for both batch and interactive query patterns. Experience with schema evolution and versioning strategies, including managing model changes in production without breaking downstream consumers. Comfort with async collaboration across time zones, including clear documentation, self-explanatory model diagrams, and proactive communication. Familiarity with Nike's internal data platforms (Sole, Databricks on Nike infrastructure). Personal background in endurance sports, coaching, or sport science. Experience with data privacy modelling for health/fitness data, including PII handling and consent-based access patterns. Experience modelling for ML/AI consumption, such as feature stores, training datasets, or model input/output schemas. The Application Process Accommodations are available to complete the interview process, including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. Request an accommodation if needed due to a disability. About the Company We're MAC BI, the business intelligence function for Nike's Marketing, Activity, and Converse organisations. #J-18808-Ljbffr Global Sports Jobs Limited