Technology Integration Manager

Astreya; Santa Clara (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Astreya is seeking a Senior Technical Program & Integration Manager to own the end-to-end integration architecture for global supply chain risk data. You will lead the BI and data-engineering efforts, set governance standards, and establish scalable data pipelines to support dashboards and predictive models.

You will optimize the GPS/telemetry shipment-visibility program, standardize data handoffs, and implement automation across ingestion, event handling, and reporting.

Qualifications

• 8+ years of technical program management, systems integration, or architecture leadership.
• Ownership of end-to-end architecture and governance for enterprise data platforms.
• Experience delivering API/data-pipeline integrations and event-driven workflows.
• Experience guiding data and BI engineers and approving design decisions for critical components.
• Ability to translate business needs into data-driven roadmaps, SOPs, and governance artifacts.

Responsibilities

• Own end-to-end integration architecture for the risk data estate and define data schemas.
• Define data standards, governance across sources, and automated data-quality checks.
• Direct BI and data-engineering to build scalable ingestion pipelines and review designs.
• Develop data-quality scoring, monitoring, and alerting for drift and ingestion failures.
• Consolidate data layers to serve dashboards and predictive risk models.
• Prepare data foundation for AI/ML readiness and real-time scoring.
• Map shipment-visibility processes and standardize data handoffs across partners.
• Create controls for missing data, mismatches, and activation gaps.
• Develop automation opportunities in shipment creation and telemetry ingestion.
• Establish playbooks, SOPs, and governance for a scalable risk platform.
• Support global reporting for damage, loss, and incident data with clear taxonomy.

Skills

PM leadership

Data governance

API integrations

BI leadership

Data models

Stakeholder comms

Job description

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.

Primary Responsibilities

1. Technical Leadership: Integration Architecture and Data Governance

• 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.

2. GPS/Telemetry Shipment-Visibility Program Optimization

• 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.

4. Internal Risk Platform: Program, Integration, and Operational Readiness

• Translate business outcomes into a prioritized product and integration roadmap with milestones, dependencies, risks, acceptance criteria, and release-readiness gates.
• Coordinate internal AI partners, engineering, security, data owners, and operations; maintain clear decision logs, interfaces, and escalation paths.
• Define the target integration architecture, canonical data model, data contracts, source ownership, identity and matching rules, event taxonomy, and audit requirements.
• Establish secure, observable ingestion for APIs, files, and streams, including retry, reconciliation, error handling, data lineage, and access controls.
• Design human-in-the-loop AI use cases for prioritization, enrichment, summarization, and recommendation while retaining explainability, approval controls, and a clear fallback process.
• Create release, test, incident, change, support, and service-management processes appropriate for a smaller team and an evolving internal platform.

5. Global Damage, Loss, and Incident Reporting

• Establish a global internal record for transportation and warehouse events, including incidents that do not become claims, with data that supports scorecards, trend analysis, and internal reporting.

6. Cross-Platform Process and Data Foundation

• Create one operating taxonomy for event, incident, shipment, supplier, location, severity, status, owner, action, outcome, and closure reason.
• Establish a single operational backlog and decision process across platform enhancements, data defects, vendor issues, automation candidates, and process improvements.
• Define service levels, operating hours, escalation expectations, control evidence, audit retention, access reviews, and business-continuity procedures.
• Create dashboards for operational health, data quality, vendor performance, risk-event outcomes, workload, aging, and automation benefits.
• Use lightweight continuous-improvement methods (baseline, prioritize, pilot, measure, standardize) and retire manual work only after replacing controls are proven.

Required Qualifications

• 8+ years of technical program management, systems integration, platform delivery, data products, or related architecture leadership experience, including demonstrated ownership of end-to-end architecture and governance for enterprise data platforms.
• Demonstrated delivery of API/data-pipeline integrations, event-driven workflows, data models, quality controls, observability, and production-support processes.
• Experience directing data and BI engineers, reviewing architecture decisions, and approving design documents for critical pipeline components.
• Experience designing operating models that combine automation, case/workflow management, service levels, human judgment, and audit trails.
• Strong knowledge of supply-chain execution, shipment visibility, logistics risk, physical security, supplier risk, or business continuity.
• Experience with third-party platforms and vendors, including technical onboarding, contract/service performance, defect escalation, and roadmap influence.
• Ability to work effectively with AI/ML teams and convert AI capabilities into governed, measurable, human-in-the-loop operations.
• Excellent written and verbal communication; able to translate technical issues into business impact and produce usable SOPs, diagrams, and decision records.
• Proven history of reducing manual work and leaving behind sustainable processes, documentation, controls, and trained internal owners.

Preferred Qualifications

• Hands-on familiarity with GPS/IoT telemetry platforms, disruption-event management platforms, incident/case systems, or comparable supply-chain risk tooling.
• Experience in semiconductor, high-value cargo, global manufacturing, technology supply chains, or 24x7 command-center operations.
• Knowledge of secure software delivery, privacy, access controls, audit readiness, data governance, and AI risk management.