Description

Job Description

The following expectations applied:

• Senior or lead-level architect and hands-on practitioner, able to take initiatives from concept through design, architecture, and working prototypes rather than strategy alone. 
• Deep data and information architecture: conceptual, logical, canonical, and semantic data models; authoritative systems of record and reusable data products; source-to-target mapping, data lineage, reconciliation, auditability, metadata, and governance. 
• Modern integration and platform fluency across APIs, event streaming (Kafka), ETL/ELT, data virtualization, Lakehouse consumption, and cloud or object storage such as NetApp S3. 
• Financial-services domain depth across risk, compliance, finance, and regulatory contexts, with the ability to translate business and regulatory needs into scalable architecture. 
• AI-embedded delivery: applies GenAI, agentic AI, LLM/RAG, and knowledge-graph techniques to real use cases and rapid prototypes, while keeping AI governed, explainable, and compliant with Responsible AI, Model Risk, privacy, and security expectations. 
• Strong communicator and influencer with executive presence, comfortable presenting to senior leaders and Architecture Review Boards and operating independently in ambiguous, matrixed environments. 
• Typically 7+ years in engineering and solution architecture, with 5+ years in a relevant financial-services domain, and familiarity with an enterprise architecture framework such as TOGAF.
• Own the data information strategy, target-state architecture, and sourcing roadmap for the enterprise Testing & Monitoring platform. 
• Model the core GRC domains (Risk, Control, Obligation, Issue, Test, Process, and Evidence) with canonical, semantic, and business-glossary definitions. 
• Define sourcing from authoritative systems of record and design ingestion using APIs, ETL/ELT, event streaming, virtualization, and metadata-driven patterns. 
• Enable configurable testing, risk-based sampling, control-effectiveness measurement, automated evidence collection, and end-to-end regulatory traceability. 
• Apply Neo4j, knowledge graphs, and ontologies for lineage, impact analysis, and cross-domain risk intelligence. 
• Embed AI for anomaly detection, intelligent sampling, evidence summarization, obligation-to-control mapping, and predictive issue identification. 
• Nice to have: experience data catalog, metadata, and lineage tooling

Key Skills

Education

Any Graduate