Technology Integration Manager

Role:
Technical Program & Integration Manager - Global Supply Chain Security & Risk Platforms Job Description Role Mission

Own the end-to-end integration and data architecture for supply-chain risk; optimize the GPS/telemetry shipment-visibility program and the disruption event-to-action model; bring the internal Risk Platform from build into reliable operational use; deliver a global damage, loss, and incident record; and prepare the data foundation for AI/ML-driven predictive risk. This is a senior, architecture-owning role: the successful candidate directs the BI and data-engineering effort, approves design documents, and establishes the data standards and governance framework that every downstream risk platform depends on.

• Integration architecture for supply-chain risk. Design and own the end-to-end integration architecture for the risk data estate, including APIs, data pipelines, and the platform interoperability layer, and provide technical direction on schema modeling and ingestion patterns.
• Data standards and governance. Define and enforce data normalization frameworks, naming conventions, and governance across all supply-chain risk data sources; build automated data-quality checks for duplicates, inconsistencies, and latency gaps.
• Pipeline development oversight. Direct the BI and data-engineering effort to build scalable, automated ingestion and processing pipelines; review architecture decisions, approve design documents, and conduct reviews on critical pipeline components.
• Data-quality remediation program. Identify and resolve data-quality issues across systems; define a data-quality scoring methodology and KPIs; build monitoring and alerting for drift detection, schema changes, and ingestion failures.
• Unified data strategy. Reduce dependency on fragmented vendor tools through a consolidated data layer; build normalized, query-ready datasets that serve both operational dashboards and predictive risk models.
• AI/ML readiness and platform evolution. Prepare the data foundation for AI/ML-driven analytics and predictive risk modeling, ensuring architecture supports feature engineering, model training data extraction, and real-time scoring integration.

• Map the end-to-end process from shipment selection and device request through activation, association, monitoring, exception response, completion, and reporting.
• Standardize roles, data fields, handoffs, approval points, service expectations, and escalation paths across the client, logistics partners, suppliers, the Command Center, and the platform provider.
• Create controls for missing or late device data, incorrect shipment/device association, coverage gaps, delayed activation, sensor exceptions, and incomplete closeout records.
• Identify automation opportunities for shipment creation, status synchronization, telemetry ingestion, exception creation, notifications, evidence capture, and KPI reporting.
• Establish vendor scorecards, recurring service reviews, root-cause analysis, and a prioritized improvement backlog; maintain SOPs, training, checklists, and quality audits so the program is repeatable.
• Document current event sources, categories, thresholds, duplication and noise, enrichment needs, stakeholder routing, escalation, response, closure, and reporting.
• Create a common severity and disposition model so events are handled consistently across suppliers, locations, lanes, shipments, and business impacts.
• Improve entity matching between disruption events and internal data (suppliers, sites, shipments, products, owners); define confidence thresholds and exception queues.
• Reduce manual effort in case creation, assignment, reminders, escalation, status updates, and closure evidence wherever reliable automation is possible.
• Develop Command Center playbooks by event type, severity, time sensitivity, region, and business impact, with automated feedback loops that measure whether alerts were actionable and timely.

• Translate business…