At a Glance
- Tasks: Design innovative data architectures for AI/ML workloads and collaborate with cross-functional teams.
- Company: Leading tech firm in London focused on vehicle payment systems.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Exciting projects with real-time impact and excellent career advancement opportunities.
- Why this job: Join a dynamic team to shape the future of data architecture and AI technology.
- Qualifications: 10+ years in data architecture with strong cloud-native platform experience.
The predicted salary is between 80000 - 100000 £ per year.
Job Location: London, UK/Hybrid
Job Type: Permanent
Key Responsibilities
- Data & AI Architecture
- 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.
- Strong experience designing cloud-native data platforms (AWS preferred).
- 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.
Data Architect in London employer: Falcon Smart IT (FalconSmartIT)
As a Data Architect at our London-based company, you will thrive in a dynamic and innovative work culture that prioritises collaboration and continuous learning. We offer competitive benefits, including flexible hybrid working arrangements, professional development opportunities, and a commitment to diversity and inclusion, ensuring that every employee can grow and contribute meaningfully to our mission of transforming vehicle payment systems through cutting-edge data architecture and AI solutions.
Contact Details:
Falcon Smart IT (FalconSmartIT) Recruitment Team
StudySmarter Expert Advice🤫
We think this is how you could land Data Architect in London
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just grab a coffee with someone who’s already in the data architecture game. You never know when a casual chat could lead to your next big opportunity.
✨Show Off Your Skills
Don’t just talk about your experience; showcase it! Create a portfolio of projects that highlight your data architecture skills, especially those related to AI/ML. This will give potential employers a clear picture of what you can bring to the table.
✨Ace the Interview
Prepare for interviews by brushing up on common data architecture questions and scenarios. Be ready to discuss your past projects and how they align with the role. Remember, it’s not just about answering questions but also about showing your passion for the field!
✨Apply Through Our Website
We’ve got some fantastic opportunities waiting for you! Make sure to apply through our website for the best chance at landing that dream job. Plus, it’s a great way to stay updated on new openings tailored to your skills.
We think you need these skills to ace Data Architect in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Data Architect role. Highlight your experience with data architectures, AI/ML workloads, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data architecture and how your background makes you a perfect fit for our team. Keep it engaging and personal – we love a good story!
Showcase Your Technical Skills:Don’t forget to showcase your technical skills in your application. Mention your experience with cloud-native platforms, distributed data processing, and any certifications you have. We’re keen to see how you can contribute to our data-driven initiatives!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It’s super easy, and you’ll be able to submit all your documents in one go. Plus, it helps us keep track of your application!
How to prepare for a job interview at Falcon Smart IT (FalconSmartIT)
✨Know Your Data Inside Out
Make sure you’re well-versed in the specifics of data architecture, especially around AI/ML workloads. Brush up on your knowledge of cloud-native platforms like AWS and be ready to discuss your experience with distributed data processing and data-lake architectures.
✨Showcase Your Collaboration Skills
Since this role involves working closely with Data Science teams and software engineers, prepare examples that highlight your ability to collaborate effectively. Think about times when you’ve successfully partnered with different teams to achieve a common goal, especially in the context of data governance and quality.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions during the interview. Be ready to explain your approach to designing scalable data pipelines and how you ensure data quality and compliance with regulations like PCI and GDPR. Practise articulating complex concepts in a way that’s clear to both technical and non-technical stakeholders.
✨Demonstrate Continuous Improvement Mindset
This role is all about driving improvements in data reliability and scalability. Come prepared with examples of how you’ve implemented changes in past roles that led to better performance or efficiency. Highlight your understanding of ML Ops practices and how they can enhance model deployment and monitoring.
