Senior Analytics Engineer, Service Parts

Role Summary

The Analytics Engineer, Global Distribution Strategy (GDS) for Service Parts, is tasked with driving operational excellence through the rigorous governance and standardization of Service Parts Master Data and other essential service parts data, ensuring defined targets and thresholds are established and maintained. A core responsibility is to oversee the monitoring, standardization, and governance of Service Parts data to facilitate process adherence or enhancements within the Service Parts function. This individual will be responsible for the development and maintenance of a comprehensive Service Parts Quality Index. This consolidated scorecard will assess data integrity across key dimensions, including, but not limited to, Accuracy, Completeness, Timeliness, and Freshness. The Quality Index will serve as the primary mechanism for data quality and improving physical processes or defining system modifications, thereby elevating the data maturity required for analytical use across all service parts and partner organizations. In addition to establishing end to end data quality for service parts, this role will provide ongoing support for broader analytics requirements, develop robust data models, and leverage Machine Learning (ML)/Artificial Intelligence (AI) to proactively pinpoint areas for improvement within the Service Parts Organization.

Responsibilities

• Data Quality Index Management: Develop, maintain, and iterate on a comprehensive Service Parts Quality Index. Monitor data integrity across key dimensions—including Accuracy, Completeness, Timeliness, and Freshness—to serve as the primary truth for domain health.
• Standardization & Governance: Drive operational excellence by establishing rigorous governance standards for Service Parts Master Data. Define and maintain clear targets and thresholds to ensure data remains "analytical-ready" for the entire organization.
• Process & System Optimization: Leverage data quality insights to champion improvements in physical warehouse and distribution processes or advocate for essential system modifications across Rivian's ERP systems, including SAP, PTC (Servigistics), OTM, and Service Operating Systems.
• Advanced Analytics & Modeling: Support broader GDS requirements by developing robust, scalable data models. Leverage Machine Learning (ML) and Artificial Intelligence (AI) to proactively pinpoint inefficiencies and suggest predictive improvements within the Service Parts network.
• Cross-Functional Stewardship: Facilitate process adherence across Service Parts functions. Collaborate with partner organizations to elevate data maturity and ensure a consistent data language across the global distribution strategy.
• Reporting: Translate the Quality Index and ML-driven insights into actionable scorecards, highlighting the correlation between data maturity and distribution performance.

Qualifications

• Education: Bachelor’s or Master’s degree in Data Science, Business Analytics, Supply Chain Management, or Engineering.
• Technical Expertise: Proficiency in SQL and data transformation tools (e.g., dbtCloud). Direct experience building and deploying Machine Learning (ML) or AI models to solve operational problems.
• Data Governance Mastery: Proven track record in establishing data governance frameworks, specifically defining data quality dimensions, thresholds, and standardized MDM processes.
• Domain Knowledge: 5+ years of experience in Service Parts, Global Distribution, or Supply Chain Analytics. Deep familiarity with SAP Master Data and its impact on physical distribution logic.
• Analytical Depth: Experience developing complex data models and "Quality Index" scorecards that drive business-critical decisions.
• Strategic Communication: Ability to bridge the gap between technical data engineering and physical operations, effectively communicating data maturity needs to both technical teams and executive leadership.
• Systemic Thinking: A proactive mindset focused on "system modifications" rather than manual workarounds to ensure long-term data scalability.