Data Modelling Architect

Location: East Quogue, United States

Department: Development

Experience: 15+ years

• 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 behaviour.
• 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.

• 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 modelling efforts across conceptual, logical, and physical layers, applying best practices in normalized (3NF) and dimensional modelling to support complex, high-volume financial datasets and enable effective reporting and analytics.
• Create and maintain semantic/canonical 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.
• Serve as a trusted, domain-aware technical partner to engineering teams to providing guidance and hands-on support to ensure architectural integrity and delivery excellence.
• 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.
• Integrate AI Agents and MCP-based capabilities with enterprise data platforms, enabling secure, governed access to data, tools, and services through scalable and production-ready patterns.
• Design agent-ready data and context architectures leveraging knowledge graphs, semantic models, metadata, and APIs, with strong controls for security, observability, auditability, reliability, and graceful failure.
• Demonstrate practical expertise with cloud platforms (AWS, Azure, GCP), leveraging managed services, multi-tenant architectures, and balancing cost-performance trade-offs effectively.

• 15+ years building and operating production systems, with increasing depth of expertise at each level.
• Proven hands-on experience in designing and implementing scalable data architecture and comprehensive data modelling (conceptual, logical, and physical) for large-scale enterprise systems using tools like Erwin/RStudio.
• Strong engineering background with the ability and willingness to remain close to the code, implementation, and production environment.
• Strong hands-on expertise in semantic data engineering, ontology development, RDF/OWL, SPARQL, SHACL, and knowledge graph technologies, with experience designing scalable semantic models, validation frameworks, and graph-based solutions.
• 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, lineage throughout and operational resilience throughout these workflows.
• In-depth knowledge about Agentic solutions and MCP-based integrations, with a strong understanding of agentic architectures, enterprise data access, tool integration, security, governance, observability, and production reliability.
• Proven experience building large-scale, distributed, data-intensive, and event-driven systems with cloud-native architectures preferably within financial services or another highly regulated industry.

• Demonstrated ability to lead through technical credibility & hands-on contribution.
• Strong communication skills, with the ability to engage effectively with engineers, engineering managers, product leaders, architects, and senior segment leadership.
• Able to operate effectively across multiple delivery pods, balancing strategic architectural direction with day-to-day engineering realities.
• A strong sense of ownership for outcomes, with the ability to stay engaged from architecture through implementation and production operation.