The Role
Lead hands-on enterprise data architecture and engineering across multiple delivery pods. Design scalable cloud-native data platforms, ingestion pipelines, data meshes, semantic models, knowledge graphs, and analytical layers. Develop conceptual, logical, physical, normalized, and dimensional data models, applying RDF, OWL, SPARQL, and SHACL. Support production coding, integrations, performance, incidents, and reliability. Integrate AI agents and MCP capabilities with secure, observable enterprise data platforms while advising engineering and product leadership.
Summary Generated by Built In
Role: Enterprise Data Architect
Location: Remote (New Jersey & Dallas preferred)
About the Role:
The Team:
The Enterprise Solutions Technology division includes workflow solutions across Loan & Credit Workflow, Client Onboarding & Entity, Post-Trade, Tax & Regulatory Reporting, Public & Private Markets and Pricing, Public Valuations, Reference Data & Risk Analytics (FRA) & other shared functions.
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 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.
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 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.
What are we looking for
- 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.
Leadership & Influence
- 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.
Skills Required
- 15+ years building and operating production systems
- Hands-on experience designing scalable data architectures for large-scale enterprise systems
- Comprehensive conceptual, logical, and physical data modeling experience
- Experience with Erwin or RStudio
- Strong engineering background with hands-on coding and production environment experience
- Semantic data engineering and ontology development experience
- Hands-on expertise with RDF, OWL, SPARQL, SHACL, and knowledge graph technologies
- Knowledge of financial domain workflows, including trade lifecycle, processing, reporting, and reconciliation
- Knowledge of agentic solutions and MCP-based integrations
- Experience with enterprise data access, tool integration, security, governance, observability, and production reliability
- Experience building large-scale distributed, data-intensive, event-driven systems
- Practical expertise with AWS, Azure, or GCP
- Ability to lead through technical credibility and hands-on contribution
- Strong communication skills across engineering, product, architecture, and senior leadership
- Financial services or highly regulated industry experience
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The Company
What We Do
@TechBlocks we power the software defined industries (SDI) of today and tomorrow. We are a software engineering and consulting firm. We build modern digital value chains and businesses reimagined to create frictionless experiences for innovative monetization methods and drive unforeseen efficiencies. We are known to build world class custom platforms and products that are cloud native for some of the worlds largest brands. We are the go to technology partners for born in digital businesses that grew with us from "Concept to Commercialization" and have revenues between $100M - $10B. We help modern businesses transition just from a technology outsourcing mentality to help create globally distributed digital COEs and mature them. Our converged COEs that we create in partnership with our clients help power software factories that are extremely dynamic. We have created modern digital COEs and factories that are created with a single minded goal to future proof our clients businesses. Everything we do is centred around two philosophies and practices - Design Thinking and Lean Engineering. Whether it is building digital commerce platforms, marketplace for worlds largest retailers or smart utilities applications and products or digital health products/platforms that power wearables, patches or devices across healthcare landscape; we do it all with speed and sophistication that is unmatched in the industry
