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Company Overview
A leading global payments and financial platform empowers businesses worldwide with fully integrated solutions for accounts, payments, spend management, treasury, and embedded finance. The company combines proprietary infrastructure and software to support over 200,000 businesses globally, delivering scalable, innovative financial technology solutions. With a strong presence across 26 offices and valued at US$8 billion, it is backed by top-tier investors and is committed to building the next generation of global banking infrastructure.
Summary
The Principal Architect will operate at the intersection of platform architecture, data products, and AI enablement. The role focuses on architecting a governed, reusable data, knowledge, and skills layer that powers analytics, AI agents, and real-time decision systems. This senior individual contributor position drives the evolution of the company’s data ecosystem into a fully AI agent-ready infrastructure, enabling actionable insights and high-quality decision-making across the business.
Responsibilities
Own the technical execution of regional and global data localization strategies within the Databricks environment
Design scalable patterns that satisfy regulatory requirements while maintaining a unified global data model
Reconcile local compliance constraints with global consistency in definitions, metrics, and knowledge artifacts
Partner with Data Science, AI Engineering, Product, and Risk teams to support experimentation and data-driven decision-making
Lead global data modeling efforts and create unified schemas accommodating regional variance
Define and implement “Agent Interfaces”—APIs and tools enabling AI agents to interact with Knowledge and Skills layers
Implement Governance-as-Code, embedding automated data lineage and quality controls into the platform
Architect the three-layer platform structure:
Data Layer: Governed, high-quality structured datasets with strong lineage and regional compliance
Knowledge Layer: Semantic layer defining business metrics, entity relationships, and contextual documentation
Skills Layer: Operationalized capabilities including reusable workflows, agent-callable tools, automated pipelines, and transformation primitives
Minimum Requirements
10+ years of experience designing and building large-scale data systems with strong architectural fundamentals
Hands-on experience with Databricks ecosystem, including Unity Catalog and Delta Lake
Experience implementing semantic layers or metric stores for consistent reporting and analytics
Strong understanding of AI/agent-based data consumption and experience building APIs or data services
Experience designing solutions that meet regional data residency and compliance requirements
Proven 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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