Data Architect

Technologies we use

Expected

• AWS
• Docker

Optional

• Python
• SQL
• Kafka
• Redis
• PostgreSQL
• TensorFlow
• PyTorch
• Azure DevOps
• Figma

About the project

Define and evolve the data architecture needed to power advanced analytics, machine learning, and AI driven features such as fraud detection, risk scoring, spend optimization, and predictive vehicle insights.

Design robust, secure, real time data platforms that enable our client to deliver next generation international vehicle payment capabilities.

This is how we organize our work

Team size

20+

This is how we work

• in house
• you focus on a single project at a time
• you have influence on the technological solutions applied
• you have influence on the product
• agile

Team members

• backend developer
• technical leader
• architect
• devOps
• data scientist
• project manager

Your responsibilities

• Plan end to end data architectures that support AI and ML workloads using structured, semi structured, and unstructured data
• Develop data models, canonical schemas, entity definitions, and integration approaches for international vehicle payment workflows
• Architect scalable data pipelines that handle ingestion, transformation, feature engineering, and model deployment
• Define long term data architecture strategy aligned with the platform roadmap
• Ensure the data architecture supports explainable AI, responsible AI, and transparent model evaluation
• Collaborate with data science teams to enable a unified feature store, ML registry, and model ready datasets
• Implement real time and near real time data flows for fraud detection and authorization decisioning
• Evaluate and recommend AI and ML technologies, vector stores, model operations platforms, and data platforms
• Enable secure integration of generative AI and predictive AI into customer and operator facing solutions
• Establish data quality, lineage, metadata, and cataloguing standards
• Partner with security and compliance teams to align with PCI, GDPR, and financial services data requirements
• Define and support policies for data retention, PII handling, model transparency, and AI governance
• Collaborate with software engineering teams to embed data centric design principles into product architecture
• Provide architectural guidance on APIs, microservices, and event driven systems
• Conduct architectural reviews, create reference architectures, and mentor engineers
• Drive improvements in data reliability, platform scalability, and cost efficiency

Our requirements

• 10+ years of experience in data architecture, solution architecture, or related roles
• Experience designing cloud native data platforms in AWS
• Knowledge of distributed data processing, data lake and lakehouse architectures, streaming platforms, feature stores, and model serving patterns
• Understanding of ML operations practices including CI/CD for ML, automated retraining, and model monitoring
• Experience designing or supporting AI or ML based products
• Understanding of security and regulatory requirements for financial data
• Ability to communicate clearly with technical and non technical stakeholders

Optional

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

This is how we work on a project

• Continuous Deployment
• Continuous Integration
• DevOps
• documentation
• test automation

Development opportunities we offer

• conferences abroad
• conferences in Poland
• mentoring
• substantive support from technological leaders
• technical knowledge exchange within the company

DataArt Poland sp. z o.o.

Our client is modernizing and scaling its data ecosystem to support intelligent, real time, secure, and compliant fleet and vehicle related payment solutions.