Lead Data Architect _Exp: 14+ Years

About the Role:

The Team:

The Enterprise Solutions Technology division includes workflow solutions across Lending, Reg & Compliance, Global Markets and Software business. Enterprise Solutions provides industry-leading set of integrated tools, solutions and data services that help our clients make efficient investment decisions, operate with greater efficiency, and provide transparency across their business and to their key stakeholders.

What’s in it for you:

We are seeking a Leader (Data Architecture) to work across delivery pods within a segment, partnering closely with segment technology leads, senior engineers, and product teams to ensure systems are designed and built correctly.

This is a hands-on architecture and engineering role, not a governance or review-only position. The role exists to ensure architectural decisions are grounded in real implementation constraints, carried through into code, and result in systems that perform reliably in production.

You will work, day to day with delivery pods to shape designs, review and write code where needed, and resolve complex technical issues. You will be involved early in design discussions and remain engaged through build, release, and production operation.

You will step in on complex integrations, performance issues, failures, and incidents, helping teams stabilize systems and improve them based on real production behavior.

You will operate across multiple pods within a segment, influencing design and delivery through hands-on contribution, technical review, and earned credibility rather than formal authority.

Responsibilities:

• Design and implement scalable, end-to-end data architectures encompassing source system ingestion, integration pipelines, cloud-native data platforms, data mesh and downstream analytical and operational consumption layers, ensuring robustness, scalability, and security.
• Lead comprehensive data modeling efforts across conceptual, logical, and physical layers, applying best practices in normalized (3NF) and dimensional modeling to support complex, high-volume financial datasets and enable effective reporting and analytics.
• Create and maintain semantic data models using ontologies and validate data integrity via SHACL shapes to build enterprise knowledge graphs, establishing consistent terminology, enforcing data quality constraints, enhancing data interoperability.
• Possess strong knowledge of end-to-end workflows relevant to financial domains (e.g., trade lifecycle, processing, reporting, reconciliation), and design data architectures that optimize data movement, quality, and lineage throughout these workflows.
• Drive execution of technology strategies by translating business and technical requirements into actionable short-to-medium term technology roadmaps aligned with organizational goals.
• Integrate AI/ML or agent-enabled components into data architectures with a focus on reliability, explainability, operational controls, and graceful failure handling.
• Design and enforce data quality frameworks incorporating metadata management, lineage tracking, controls, and auditability to meet regulatory and operational compliance requirements.
• Analyze domain-specific failure modes—such as late or missing data, data mismatches, reconciliation discrepancies, and regulatory deadlines—and architect systems that proactively detect, surface, and remediate these issues.
• Serve as a trusted, domain-aware technical partner to engineering managers and delivery teams, providing guidance and hands-on support to ensure architectural integrity and delivery excellence.
• Possess strong experience designing and building distributed, data-intensive, event-driven systems within financial services or similarly regulated environments, emphasizing resilience and scalability.
• Produce detailed architectural designs and reference implementations; collaborate closely with engineering teams to translate designs into production-quality systems.
• Exercise sound judgment regarding system behaviour in production environments, including performance optimization, failure mode management, resilience, recovery strategies, and risk isolation.
• Demonstrate practical expertise with cloud platforms (AWS, Azure, GCP), leveraging managed services, multi-tenant architectures, and balancing cost-performance trade-offs effectively.
• Maintain hands-on involvement in reviewing, writing, and improving production code; collaborate with senior engineers to solve complex technical challenges and ensure high-quality deliverables.

What are we looking for

• 15+ years building and operating production systems, with increasing depth of expertise at each level.
• Mandatory: Proven hands-on experience in designing and implementing scalable data architecture and comprehensive data modeling (conceptual, logical, and physical) for large-scale enterprise systems.
• Strong hands-on engineering background; comfortable being close to the code and delivery work.
• Optional: Experience in knowledge graph engineering, including ontology development, semantic web technologies, and SHACL validation.
• Pragmatic and delivery-focused; prioritizes practical solutions over theoretical perfection.
• Communicates clearly with engineers, product partners, and segment leadership.
• Leads through credibility, presence, and technical contribution, not hierarchy.