Data & Information Architect, Testing & Monitoring (TANDMS)

Role: Data & Information Architect, Testing & Monitoring (TANDMS) Duration: 6-12+ Months Contract Location: Charlotte, NC or Minneapolis, MN - Hybrid (3 Days onsite)

Job Description:

• 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.

Must have:

• Aligned to the enterprise Testing & Monitoring (TANDMS) platform within Corporate Risk and Governance, Risk & Compliance (GRC).

• 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 with data catalog, metadata, and lineage tooling