Senior Data Architect - Supply Chain

About

Meta's Reality Labs Supply Chain is seeking a Senior Data Architect to define the target-state performance data architecture for supply chain measurement. In this role, you will design metric models, KPI taxonomies, scorecard frameworks, and alerting architectures that give the organization a single, consistent view of supply chain performance across planning, manufacturing, logistics, quality, and fulfillment. You will serve as the technical authority on performance data modeling and measurement architecture, bringing metrics into a unified measurement layer structured for executive reporting, operational alerting, and AI-driven decision-making.

Responsibilities

• Define and maintain the performance data model—a unified schema representing supply chain KPIs across all domains with consistent entity definitions, time grains, and dimensional hierarchies
• Own the performance layer reference architecture, defining how data flows from source systems through transformation into metric computation engines and serving layers (dashboards, scorecards, alerts, AI agents)
• Design the metric computation framework with standardized patterns for KPI calculation (actuals vs. targets, rolling averages, period-over-period, statistical process control) that replace ad-hoc approaches
• Own the KPI taxonomy—the hierarchical structure connecting executive-level metrics (OTIF, total cost, quality index) to operational drivers to root-cause indicators
• Define metric certification standards and the governance process metrics must pass before appearing in official reporting
• Design the threshold and target management framework, organizing how targets cascade from VP-level to operational bounds, including seasonal adjustments and plan changes
• Architect the scorecard computation engine, determining how individual KPIs roll up into composite scores with consistent red/amber/green status for leadership reviews
• Design the performance alerting framework with threshold-based alerts, trend-break detection, and anomaly identification that distinguish real degradation from noise
• Drive alignment on metric definitions and measurement standards across all functions and programs without direct authority, serving as the technical authority in architecture reviews
• Mentor analysts, data engineers, and BI developers in performance data modeling; establish measurement architecture standards that become self-sustaining across the organization

Minimum Qualifications

• 10+ years of experience in data architecture, analytics engineering, or BI platform architecture with progressive scope
• Experience with dimensional modeling, metric computation frameworks, and performance management system design
• Experience with modern data platforms (Databricks, Spark, Snowflake, or equivalent) at enterprise scale
• Experience designing KPI frameworks and measurement systems for complex operations (supply chain, manufacturing, logistics, or comparable)
• Experience with statistical process control, time-series analytics, and anomaly detection approaches
• Track record of defining metric governance and KPI standardization across multi-team organizations
• Experience influencing senior leadership and driving cross-functional alignment without direct authority Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Supply chain or manufacturing performance management experience (SCOR model, OTD/OTIF measurement, yield tracking)
• Experience translating complex measurement architecture into business impact for non-technical audiences
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience with AI/ML approaches for anomaly detection in operational metrics
• Experience with real-time and near-real-time data architectures (streaming, CDC, event-driven patterns)
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)