Information Architect

Data & Information Architect, Testing & Monitoring (TANDMS)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.We are looking for a builder-minded architect who pairs strategic vision with hands-on execution: someone who structures ambiguity, makes sound decisions, and drives initiatives independently. The strongest candidates combine data-architecture depth, financial-services domain fluency, and practical AI skill to deliver solutions that are governed, resilient, and auditable, and they bring the executive presence to align business, risk, data, and engineering stakeholders.