SVP, Data & AI Strategy and Transformation

Kapitus is seeking a Senior Vice President, Data & AI Strategy and Transformation to serve as the Chief Data and AI Officer’s principal leader for enterprise Data & AI architecture, transformation execution and technical delivery. Reporting to the CDAO, the SVP will translate and execute Kapitus’ approved enterprise Data and AI vision to sequenced target state architecture, operating mechanisms, investment roadmap, reference patterns, and measurable transformation outcomes.

The SVP will lead the practical conversion of fragmented data, workflows and point solutions into trusted enterprise data, reusable intelligence, governed automation, and measurable business outcomes. This is a player-coach role for a leader who move fluently between executive decision making and technical implementation. Will be advising the CDAO on material choices while remaining directly engaged in architecture, prototyping, design assurance, and delivery.

 

What you will do:

Strategy and Operating Model

• Lead the development and execution of the enterprise Data and AI strategy for CDAO and Executive team approval by translating corporate priorities in sales, credit, risk, finance, and operations—into a sequenced transformation plan with clear outcomes, milestones, investments, and executive decision points.
• Design, operationalize and continuously improve the Data and AI operating model including decision rights, product ownership, domain accountability, architecture governance, responsible-AI controls, funding, and value realization — and advise the CDAO and executive leadership on trade-offs, sequencing, vendor choices, and platform economics.

Enterprise Data Architecture and Data Products

• Design and implement the enterprise data architecture — data-fabric capabilities (metadata-driven integration, active lineage, policy enforcement, discoverability) with data-mesh principles (domain ownership, product thinking, federated governance) on a layered Lakehouse/medallion foundation — with reference architectures, standards, data contracts, and guardrails spanning integration, APIs, orchestration, metadata, data quality, master data, semantic and knowledge models, ML/AI, security, observability, and FinOps.
• Enable VP of Data Products to prioritize and lead data products from concept through production adoption — named owners, defined consumers, source contracts, quality thresholds, lineage, service levels, and lifecycle controls — serving certified BI, APIs, events, files, machine-learning features, and AI context.
• Provide hands-on leadership in data modeling, SQL and transformation design, orchestration, testing, observability, and production-readiness reviews, replacing duplicate pipelines, reports, and point solutions with governed, reusable products.

Agentic AI Platform and Orchestration

• Define the strategy and target architecture for governed agentic AI: platform control plane, runtime orchestration, model and provider strategy, tool integration, context engineering, memory governance, evaluation, and observability.
• Architect and build a reusable orchestration harness that binds workflows to approved skills, bounded agents, registered tools, output contracts, approval gates, stop conditions, and auditable outcomes — permitting agent autonomy only within explicitly defined scope and policy, with governed registries and reusable libraries for workflows, skills, agents, tools, context, memory, and models that separate immutable evidence from curated knowledge.
• Implement evaluation packs, baseline-versus-candidate testing, promotion gates, rollback paths, and post-deployment monitoring — Implement and enforce release controls under the AI governance policy, including evidence, approval, and rollback readiness.
• Establish governed AI-assisted engineering across architecture, code generation, documentation, testing, and review, with multi-stage review patterns ensuring no single person, model, or automation closes its own loop.

Governance and Responsible AI

• Operationalize data ownership, stewardship, metadata, lineage, quality, certification, classification, privacy, and retention as embedded platform and product controls.
• Create responsible-AI and model-governance practices — use-case intake, model risk, human oversight, explainability, fairness, monitoring, and change control — with evidence-ready traceability from business intent through data, model, agent, and outcome, so the enterprise can safely operate AI in regulated, customer-facing, credit, underwriting, and reporting environments.

Leadership, Organization and Vendor Accountability

• Lead the Data and AI Strategy, Enterprise Architecture and Transformation function working in partnership with heads of Governance, BI, Data Products, ML and Data Platform & Engineering — with a culture of technical excellence, reuse, evidence, and accountability; operate architecture review and delivery meetings, support CDAO in executive steering and AI governance forums by enabling translation of technical choices into clear business implications.
• Own vendor strategy, statements of work, acceptance criteria, and knowledge transfer — retaining internal ownership of architecture, product definitions, and platform truth — and manage transformation budget and vendor spend, tying continued investment to evidence, adoption, and measurable outcomes for CDAO and executive approval.
• Be able to enter a working session, inspect the evidence, challenge the design, build a prototype or reference pattern, troubleshoot delivery constraints, and guide the team to a production-ready outcome.
• Personally develop or review architecture diagrams, product specifications, data contracts, model and agent interaction patterns, evaluation designs, and release criteria — working directly with SQL, data models, transformation frameworks, orchestration, cloud data platforms, AI model APIs, prompts, and agent workflows when critical decisions require first-hand validation, and building reference implementations before scaling them through internal teams and vendors.
• Lead difficult design and delivery decisions from evidence rather than presentations, vendor assertions, or high-level status reporting.
• Inspire and create a motivating environment through coaching, mentoring, and enablement
• Provide opportunities to participate in both formal and informal developmental activities
• Working with senior leaders and department head, set expectations for all team members and hold employees accountable for results
• Lead hiring, performance management, compensation, and evaluation of team members
• Collaborate with Kapitus leadership and HR to continually refine and modify career growth and succession plans to meet changing business needs

 

 What we’re looking for:

• 15+ years of progressive experience across enterprise data, analytics, artificial intelligence, architecture, engineering, and technology transformation, with significant leadership responsibility in complex organizations.
• Bachelor’s degree in a technical, quantitative, or business field; equivalent executive and technical experience will be considered.
• Demonstrated success conceiving and executing enterprise Data & AI strategies, target architectures, operating models, and multi-year transformation roadmaps that produced measurable business outcomes.
• Deep hands-on experience designing and implementing data-fabric and data-mesh architectures, lakehouse/medallion platforms, semantic layers, metadata, lineage, data quality, and master data management — and personally implementing reusable data products across BI, APIs, events, files, machine-learning features, and AI consumption patterns.
• Demonstrated hands-on experience architecting and building a governed enterprise agentic AI platform or orchestration harness — workflow orchestration, reusable skills, bounded agents, tool integration, context engineering, memory governance, release gates, and rollback — beyond deploying packaged copilots, including evaluation frameworks that compare baseline and candidate behavior, detect regressions, and retain rollback evidence.
• Strong hands-on technical fluency with SQL, Python or comparable languages, data modeling, transformation frameworks such as dbt, orchestration platforms such as Dagster or Airflow, cloud data platforms such as Snowflake, and modern CI/CD, testing, security, and observability practices.
• Experience in financial services, lending, credit, payments, risk, or another regulated, data-intensive industry.
• Track record of building multidisciplinary teams, influencing C-suite stakeholders, leading major vendors, and managing budgets — with the ability to explain complex architecture and AI topics in plain business language.

Preferred:

• Graduate degree in a technical, quantitative, or business discipline.
• Experience creating original enterprise architecture, data-product, governance, or agentic-AI frameworks — including ontology and knowledge graphs, context and memory architectures, model and provider portability, prompt/context caching, and cost-optimized AI runtime design — and converting them into reusable, tested methods.
• Experience leading a mixed internal, offshore, and vendor delivery model while retaining internal architecture, product, governance, and acceptance authority.

 

Kapitus Total Rewards Package Includes: 

• Competitive Base Salary Range of $187,200-$300,300 Kapitus is providing this as a good faith salary range to comply with applicable law. The applicant’s final salary will depend on a number of factors including the applicant’s geographic location, skills, and experience.
• Annual Incentive Compensation Eligibility – Up to 20% annually