Data Architect

• Design end to end data architectures to support AI/ML workloads, including structured, semi structured, and unstructured data.
• Develop data models, canonical schemas, entity definitions, and integration patterns for international vehicle payment systems.
• Architect scalable data pipelines supporting ingestion, transformation, feature engineering, and model deployment.
• Define the long-term data architecture strategy aligned with International Vehicle Payments? technology roadmap.
• Ensure data architectures support explainable AI, bias management, and transparent model performance.

AI Platform Enablement

• Collaborate with Data Science teams to create a unified feature store, ML registry, and model ready datasets.
• Implement real time/near real time data flows required for fraud detection and authorization decisioning.
• Evaluate and recommend AI/ML technologies, vector databases, model ops platforms, and data platforms.
• Enable secure integration of generative AI and predictive AI in customer- and operator-facing use cases.

Data Governance & Quality

• Establish data quality, lineage, metadata, and cataloguing standards.
• Partner with Security and Compliance teams to ensure adherence to PCI, GDPR, and financial services data standards.
• Define and enforce policies on data retention, PII handling, model transparency, and AI governance.

Engineering & Collaboration

• Work closely with software engineering teams to embed data centric design into product architecture.
• Provide architectural guidance for APIs, microservices, and event-driven systems powering vehicle payments.
• Conduct architectural reviews, create reference architectures, and mentor engineers.
• Drive continuous improvement of data reliability, scalability, and cost efficiency.

Skills & Experience Required

• 10+ years in data architecture, solution architecture, or similar roles.
• Deep knowledge of: Distributed data processing, Data-lake/Lakehouse architectures, Streaming platforms & Feature stores and model serving.
• Understanding of ML Ops practices (CI/CD for ML, automated retraining, monitoring).
• Proven experience supporting or architecting AI/ML-driven products.
• Strong understanding of security and regulatory controls for financial data.
• Ability to communicate clearly with technical and non-technical stakeholders.

Preferred

• Experience in payment processing, fleet/vehicle telematics, or financial services.
• Familiarity with vector databases and LLM-based architectures.
• Exposure to real-time fraud detection systems.
• Certifications in Azure Data/AI, Enterprise Architecture, or similar.
• Prior experience with enterprise-scale modernization initiatives.

Success Measures

• Delivery of a scalable, reliable data and AI architecture aligned with business goals.
• Reduction in model deployment time and data preparation complexity.
• Improved real-time insights for fraud detection, spend control, and vehicle payment workflows.
• Strong partnerships across Product, Engineering, Data Science, and Compliance.
• Demonstrated uplift in data quality, governance, and platform performance.

• 10+ years in data architecture, solution architecture, or similar roles.
• Deep knowledge of: Distributed data processing, Data-lake/Lakehouse architectures, Streaming platforms & Feature stores and model serving.
• Understanding of ML Ops practices (CI/CD for ML, automated retraining, monitoring).
• Proven experience supporting or architecting AI/ML-driven products.
• Strong understanding of security and regulatory controls for financial data.
• Ability to communicate clearly with technical and non-technical stakeholders.

Preferred

• Experience in payment processing, fleet/vehicle telematics, or financial services.
• Familiarity with vector databases and LLM-based architectures.
• Exposure to real-time fraud detection systems.
• Certifications in Azure Data/AI, Enterprise Architecture, or similar.
• Prior experience with enterprise-scale modernization initiatives.

Success Measures

• Delivery of a scalable, reliable data and AI architecture aligned with business goals.
• Reduction in model deployment time and data preparation complexity.
• Improved real-time insights for fraud detection, spend control, and vehicle payment workflows.
• Strong partnerships across Product, Engineering, Data Science, and Compliance.
• Demonstrated uplift in data quality, governance, and platform performance.