Solutions / Data Architect Lead

We are looking for an architecture leader who owns the enterprise direction across solutions and data — designing, governing, and evolving the reference architectures, technical standards, and target-state blueprint that delivery teams build against. This is a principal / head-of-architecture role that combines technical authority with organizational leadership: it sits at the intersection of business meaning and technical implementation, ensuring data and solution assets are treated as strategic enterprise capabilities that reduce friction, prevent data debt, and accelerate analytics and AI adoption.

The defining shift for this seat in 2026 is engineering the architectural foundation for AI at scale. Agentic AI scales on strong data: capturing value depends on agentifying high-impact workflows, modernizing data architectures, enforcing data quality, and evolving operating models. Increasingly the mandate extends to designing and governing enterprise-grade multi-agent systems that draw on generative AI, retrieval, and vector/graph data foundations. Enterprise AI architecture is not a single tool but the combined set of data pipelines, model infrastructure, orchestration logic, integration layers, and governance controls that let AI run reliably across the organization.

The value this person brings is coherent, future-proof architecture that lets the organization scale AI-augmented solutions without accumulating uncontrolled complexity — turning unstructured data into governed, reusable assets that systems can interpret and trust.

We are seeking a principal-level architecture leader who has owned the technical direction of an enterprise's solutions and data — someone respected by engineers and able to influence executives (CIO/CTO/CDO and VPs). They lead a community of architects and set standards others follow, translating business goals and product strategy into robust, implementable platform patterns rather than diagrams that never ship.

This is a senior technical-leadership profile that partners across data engineering, analytics engineering, platform engineering, application engineering, security/GRC, product, and the ML/AI teams. It suits someone open to a hybrid or remote-first engagement, and to mobilization to the Middle East, Europe, or North America depending on the client. The person we want is authoritative yet pragmatic, a strong systems thinker who resists over-engineering, and comfortable arbitrating trade-offs across competing domains.

A strong candidate for this role has defined target-state architectures that teams actually adopted — someone who has arbitrated hard cross-domain trade-offs and made data genuinely AI-ready. They have prevented the fragmented pipelines, inconsistent definitions, and uncontrolled costs that accumulate without deliberate architecture. They bring the judgment to define reusable, governed AI patterns rather than one-off designs.

In practice, the responsibilities break down as follows:

• Architecture direction & standards: Own reference architectures and technical standards across solutions and data; define the target-state blueprint; define the data architecture framework, principles, modeling, metadata, security, reference and master data; arbitrate cross-domain trade-offs across scalability, security, cost, and maintainability.
• AI-ready data & platform: Set data architecture standards for modeling, integration, storage, governance, lineage, and quality that make customer and enterprise data secure, trustworthy, and AI-ready — including vector and retrieval data foundations across cloud, ERP, CRM, and legacy systems.
• Architecture leadership: Lead and mentor the architecture community; define and influence platform decisions across the data hub/warehouse ecosystem, MDM, governance, and metadata capabilities; review and approve major designs.
• AI-platform & agent patterns: Define governed, reusable patterns for agentic AI, foundation-model adoption, and multi-agent orchestration — including the model infrastructure, orchestration logic, and governance controls that let AI run reliably enterprise-wide.

We want to see an architect who carries real technical depth across solution and data domains and can defend designs to engineers and executives alike. They should be thinking about AI as an architectural concern — governed platforms, agent orchestration, retrieval/vector foundations, and observability — not a bolt-on. The strongest candidates treat governance as an enabler of innovation and design agents that have both the access they need and the guardrails that keep them safe.

Core architecture

• Solution and data architecture; cloud (agnostic — AWS, Azure, GCP or equivalent); integration and ETL/ELT patterns.
• Data modeling, governance, lineage, quality; MDM and metadata; security-by-design; cost optimization.
• Architecture governance and design-review practice.

Architecture performance (KPIs from research)

• Steers standards adoption, design-review quality, reuse of reference patterns, and non-functional compliance.
• Measures faster delivery of trustworthy data products, improved interoperability, lower platform/integration cost, fewer operational incidents, and stronger compliance posture.

Modern Agentic AI / AIOps (niche areas we are seeking — depth explored at interview)

This role is delivered through Solutions Chapter Group's Human Resources chapter (Chapter 12) as a Recruitment Process Outsourcing (RPO) engagement, under our Executive Search stream. Rather than a "post-and-pray" marketplace approach, we use a Curated Professional Recruitment model: candidate profiles are pre-vetted in our internal database, and when a required profile isn't already available, our dedicated Scout network conducts global headhunting to secure it. We screen and match candidates against each client's specific requirements, and placements may be remote or an on-site deployment with mobilization to the Middle East, Europe, or the United States.

From a recruitment-fit perspective, we screen for:

• Technical authority balanced with pragmatism.
• Persuasive communication across technical and business audiences.
• The ability to mentor architects and build consensus on standards.
• Long-term systems thinking without over-engineering.
• Openness to the engagement model (remote or mobilized on-site) and readiness for cross-region deployment.

Areas to explore at interview *(topics only)*

• Defining and driving adoption of a target-state architecture
• Hard cross-domain trade-off decisions

Enterprise solution & data architecture, reference architectures & standards, cross-domain trade-off leadership, architecture governance