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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Data & AI Governance Architect - **Company:** KAPITUS LLC - **Location:** United States - **Experience:** Expert - **Salary:** $117,800.0 - $189,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Control Objectives for Information and Related Technology (COBIT), Cyber Security, Information Engineering, Data Governance, Machine Learning, Metadata, Meta-Data Management, Power BI, Tableau (Software), Technical Data Management Systems, Data Logging, Data Classification, Retrieval-Augmented Generation, Snowflake, Model Validation, Generative AI, Data Layers, AI Platforms, Data Lineage, Data Management - **Published:** September 30, 2026 - **Apply:** https://www.thejobnetwork.com/job/707e05d3-d307-4f68-a474-289d7d3e6c59/principal-data-ai-governance-architect ## About the Role * 10+ years of experience in Data Governance, Data Management, Data Quality, or related data disciplines, including demonstrated success building or materially maturing enterprise governance capabilities. * Experience developing federated stewardship programs, governance operating models, policies, standards, workflows, decision rights, and governance forums. * Strong practical knowledge of business glossary design, metadata management, data cataloging, data lineage, Critical Data Elements, Data Quality, data classification, retention, lifecycle management, and certification; Atlan experience is preferred. * Working knowledge of model and AI lifecycle governance, including inventory, risk tiering, ownership, documentation, evaluation, monitoring, change management, and evidence expectations. * Direct experience in commercial or small-business lending, banking, fintech, payments, insurance, or another similarly regulated financial-services environment is strongly preferred. Ability to engage credibly with business, risk, and control stakeholders on financial-services data, reporting, and governance requirements. * Modern data-platform fluency, including Snowflake, dbt, Tableau, Power BI, Sigma, semantic layers, metadata/catalog platforms, data-quality monitoring, and automated lineage. * Working knowledge of machine-learning lifecycle concepts and modern AI patterns, including generative AI, retrieval-augmented generation, evaluation, monitoring, AI-enabled workflows, and proportionate human-oversight practices. * Exceptional communication and influence skills, with the ability to translate governance requirements into practical, repeatable processes and implementable controls for executives, business stakeholders, engineers, risk partners, and auditors. * A bachelor's degree in a relevant field, or equivalent relevant experience, is preferred; a master's degree is a plus. * Relevant certifications such as CDMP or IAPP AIGP, and NIST AI RMF or ISO/IEC 42001 training, are valued but are not required or substitutes for demonstrated implementation experience. ## Description Attention: Kapitus is aware that individuals posing as recruiters may be communicating with job seekers about supposed positions with Kapitus. Kapitus has received reports that the content and method of communication can vary, but messages may contain requests for payment (e.g., fees for equipment or training) and/or for sensitive financial information., Kapitus is seeking a high-impact, execution-oriented leader for the Principal Data & AI Governance role, serving as the enterprise governance architect and primary practitioner for its Enterprise Data Governance program. Reporting to the VP, Data Governance & BI within the CDAO organization, this senior individual-contributor role combines enterprise framework design, functional leadership, and direct operationalization. The role will architect and operationalize Kapitus's enterprise Data Governance framework while establishing the initial risk-based Model & AI Governance capability. The successful candidate will translate governance policy and standards into adoptable operating practices and increasingly automated controls across data, analytics, reporting, machine learning, generative AI, AI-enabled workflows, and applicable third-party AI services. This is a Player-Architect role for a leader who balances strategic framework design with direct operational delivery. The role defines governance requirements, control objectives, evidence expectations, and acceptance criteria; engineering and platform teams implement and operate associated technical controls. The Principal partners closely with those teams to ensure controls are practical, appropriately designed, and demonstrably operating as intended. What You Will Do * Architect and operationalize Kapitus's enterprise Data Governance framework, including policies, standards, decision rights, escalation and exception processes, evidence requirements, governance reporting, and operating cadences. * Design and activate a federated governance model that enables Business Data Owners, Business Data Stewards, and Technical Data Custodians to apply enterprise standards within their domains. * Build durable internal governance capability by codifying standards, operating practices, stewardship guidance, workflows, and decision records that reduce dependency on external consulting support over time. * Establish governance practices for critical data assets, including business glossary, Critical Data Elements, metadata, lineage, data contracts, data classification, retention, lifecycle, and certification. * Define the enterprise Data Quality framework, including quality dimensions, business rules, monitoring expectations, issue and remediation practices, risk reporting, and risk-based release or promotion criteria. * Partner with Data Engineering, Analytics Engineering, BI, Product, and Enterprise Architecture to embed governance requirements in data products, dashboards, reports, semantic models, and analytical delivery processes. * Serve as the functional product owner for Atlan and related governance capabilities, defining business priorities, workflows, adoption requirements, and evidence needs in partnership with Governance Platform Engineering. * Establish and operationalize a risk-based Model & AI Governance framework covering analytical models, machine learning, generative AI, AI-enabled workflows, agentic AI use cases, and applicable third-party AI services. * Define proportionate model and AI lifecycle requirements for intake and risk tiering, inventory and ownership, documentation, review and approval, controlled implementation, monitoring, material change, issues and exceptions, and retirement. Define governance expectations for model and AI evaluation evidence; performance, drift, and outcome monitoring where applicable; approved-data use; grounding and retrieval for generative-AI use cases; logging, access controls, traceability, human oversight, and incident response. * Establish risk-based expectations for agentic AI, including autonomy levels, tool and action permissions, human-in-the-loop or human-on-the-loop oversight, evaluation evidence, and audit trails. * Partner with Risk, Compliance, Legal, Information Security, Procurement, Product, Operations, and technical teams to align governance requirements with enterprise risk and third-party risk practices. This role does not replace independent model validation where such validation is required. ## Related Videos - [Beyond Dashboards: Fixing Text-to-SQL with Semantic RAG](https://www.wearedevelopers.com/videos/2036-beyond-dashboards-fixing-text-to-sql-with-semantic-rag) - [A Data Mesh needs Open Metadata](https://www.wearedevelopers.com/videos/505-a-data-mesh-needs-open-metadata) - [How Cisco embraced a DevOps culture within its network engineering team](https://www.wearedevelopers.com/videos/99-how-cisco-embraced-a-devops-culture-within-its-network-engineering-team) - [Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow](https://www.wearedevelopers.com/videos/1525-beyond-gpt-building-unified-genai-platforms-for-the-enterprise-of-tomorrow) - [Data: The Deciding Factor in AI Success](https://www.wearedevelopers.com/videos/100310-data-the-deciding-factor-in-ai-success) - [Crafting Custom Frameworks with Rust: A Deep Dive into Procedural Macros](https://www.wearedevelopers.com/videos/849-crafting-custom-frameworks-with-rust-a-deep-dive-into-procedural-macros) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Trustworthy AI Starts at Deployment: 5 Checks Before You Ship](https://www.wearedevelopers.com/magazine/753-trustworthy-ai-starts-at-deployment-5-checks-before-you-ship) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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