Data Architect

Falcon Smart IT (FalconSmartIT); Greater London; On-site; GBP 80,000 - 100,000; Full time

Job summary

Falcon Smart IT (FalconSmartIT) is looking for an experienced Data Architect to design end-to-end data architectures supporting AI/ML workloads. You will develop data models and ensure compliance with data standards while collaborating with engineering teams to enhance product architecture.

The ideal candidate should have over 10 years of experience in data architecture and a deep understanding of distributed processing, security, and regulatory protocols. This role requires a commitment to improving data quality and governance within the organization.

Qualifications

• 10+ years in data architecture, solution architecture, or similar roles.
• Deep knowledge of Distributed data processing and Lakehouse architectures.
• Proven experience supporting or architecting AI/ML-driven products.

Responsibilities

• Design end to end data architectures to support AI/ML workloads.
• Develop data models and integration patterns for vehicle payment systems.
• Establish data quality, lineage, metadata, and cataloguing standards.

Skills

Data architecture

Distributed data processing

ML Ops practices

AI/ML-driven products

Security and regulatory controls

Tools

Azure Data/AI

Job description

• 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.