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Company Overview
A global fintech organization is redefining the landscape of payments and financial services. The company provides a unified platform for businesses to manage accounts, payments, spend, treasury, and embedded finance on a global scale. With over 200,000 clients worldwide and a team of 2,000+ across 26 offices, it combines proprietary infrastructure with innovative software solutions. Valued at US$8 billion, it is backed by leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital. The organization is committed to driving innovation in global banking and payments.
Summary
The Principal Architect will lead the architecture of the company’s data and knowledge platforms, supporting AI, ML, and analytics initiatives. This senior individual contributor role focuses on building a governed, reusable system of data, knowledge, and skills layers to enable actionable insights, AI agents, and real-time decision-making across the organization.
Responsibilities
Own technical execution of regional and global data localization strategies within the Databricks ecosystem
Design scalable data patterns that meet regulatory requirements while maintaining a unified global data model
Reconcile local compliance constraints with global consistency in definitions, metrics, and knowledge artifacts
Collaborate with Data Science, AI Engineering, Product, and Risk teams to support experimentation and high-quality decision-making
Lead global data modeling efforts, creating unified schemas accommodating regional variance
Define and implement APIs and tools (“Agent Interfaces”) that enable AI agents to interact with Knowledge and Skills layers
Implement Governance-as-Code to embed automated data lineage and quality controls in the platform
Architect the platform’s three-layer system:
Data Layer: Governed, structured datasets including transactional records, event streams, dimensional models, and feature tables
Knowledge Layer: Semantic layer translating data into meaningful metrics, entity relationships, and documentation
Skills Layer: Operationalized workflows, agent-callable tools, automated pipelines, and transformation primitives enabling AI and analytics capabilities
Minimum Qualifications
10+ years of experience designing and building large-scale data systems with strong architectural fundamentals
Hands-on experience with Databricks, including Unity Catalog and Delta Lake
Experience implementing semantic layers or metric stores for consistent reporting and analytics
Solid understanding of AI/agent-based system data consumption, including building APIs or data services
Experience designing data solutions that satisfy regional data residency and compliance requirements
Demonstrated senior-level individual contributor experience, including leading design reviews and mentoring engineers
Disclaimer
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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
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