Artificial Intelligence Data Architect and Engineer

About Us:

Logisnext Americas Inc. has supported customers for more than 100 years as a technology-driven forklift manufacturer. In addition to being a forklift manufacturer, we are also a total solutions provider offering scalable products and services from material handling and automation to extensive fleet support.

About the role:

The AI Data Architect & Engineer plays a critical role in building and scaling the company’s long‑term data and AI capabilities. This dual-role position is responsible for both architecting enterprise-grade data platforms and hands-on development of data pipelines and integrations that support telematics, operational systems, analytics, predictive maintenance, and AI-driven applications. The ideal candidate combines strong data-engineering execution skills with architectural thinking and experience working with industrial IoT, telemetry, and other high‑volume machine-generated data. This role requires a balance of strategic design leadership and roll‑up‑your‑sleeves implementation.

What you will do:

• Design, implement, and evolve a scalable enterprise data architecture that supports telematics, operational, and enterprise data, including foundational data models, schemas, integration frameworks, and lakehouse architectures, while ensuring performance, reliability, cost efficiency, and scalability.
• Build, maintain, and optimize cloud-based data pipelines and large-scale data processing workflows to support analytics, AI, and enterprise reporting needs.
• Develop and manage ingestion pipelines for high-volume streaming and batch telemetry data from forklifts, sensors, gateways, APIs, and edge devices, ensuring reliable, scalable, and standardized data capture.
• Integrate data across enterprise systems including ERP, CRM, service management, parts and warranty systems, and vendor telematics platforms, while supporting master data alignment across assets, customers, equipment, and locations.
• Standardize, normalize, enrich, and prepare machine-generated and operational data to support predictive maintenance, operational intelligence, predictive analytics, and generative AI or RAG-based applications.
• Collaborate with IT, Analytics, Product Management, Service teams, and third-party vendors to translate business and operational requirements into scalable technical solutions, data integrations, and long-term data strategy contributions.
• Partner with internal technology stakeholders to support data governance, security, access controls, observability, DevOps/DataOps practices, and ongoing optimization of platform performance and cloud costs.

When & Where:

Hybrid Work Schedule: in the office 3 days a week. Travel less than 15%.