Senior Data Architect - Supply Chain

Job description

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

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)