Solution Architect - Enterprise Systems
Indexed description
As ZAGG’s Solution Architect -- Enterprise Systems, you will be the strategic and technical authority connecting ZAGG's business goals to our enterprise technology landscape. You will design scalable, intelligent system architectures, lead complex cross-functional programmes, and ensure every solution is purpose-built to deliver measurable business outcomes — balancing speed-to-market with long-term engineering rigour.
What You Will Do
Business Alignment & Strategic Planning
- Partner with executive, product, and commercial leadership to translate business objectives into architecture decisions with clear, measurable outcomes.
- Lead discovery workshops and requirements sessions, producing BRDs and Solution Design Documents that connect every technical decision to a KPI, OKR, or business capability gap.
- Build and present multi-year technology roadmaps to business stakeholders, articulating investment rationale, risk trade-offs, and expected ROI.
- Identify where intelligent automation and machine learning capabilities can eliminate operational friction, accelerate revenue, or unlock new business capabilities — and build the business case to pursue them.
- Facilitate prioritization workshops (MoSCoW, RICE, WSJF) to align stakeholders on scope, sequencing, and return on investment across competing initiatives.
- Contribute to vendor evaluations and RFPs, assessing enterprise platforms and emerging technology services against business requirements, scalability, and total cost of ownership.
- Design end-to-end architectures across commerce, ERP, CRM, OMS, PIM, supply chain, and data platforms — incorporating intelligent and automated capabilities as core components rather than afterthoughts.
- Define the intelligent platform layer within the enterprise architecture: the foundational models, data pipelines, orchestration patterns, and API contracts that power data-driven and automated capabilities across the business.
- Architect solutions leveraging large language models and generative capabilities — including retrieval-augmented pipelines and agentic workflows — connected to enterprise systems via governed, versioned APIs.
- Lead build vs. buy evaluations for enterprise systems and intelligent capabilities, assessing vendor platforms, foundation models, and custom development against business need and long-term maintainability.
- Define non-functional requirements across the full stack: availability SLAs, disaster recovery targets (RTO/RPO), security posture, model performance baselines, and compliance obligations (GDPR, CCPA, SOX, EU AI Act).
- Produce and maintain Architecture Decision Records (ADRs), capability maps, system-of-record designations, and architectural diagrams as the authoritative source of truth.
- Architect enterprise integrations using scalable middleware and iPaaS platforms (MuleSoft & other platforms) and event-driven messaging (AWS, SQS) — extended to support intelligent data ingestion, model feedback loops, and real-time inference.
- Define the data strategy required to power intelligent capabilities: feature stores, vector databasesreal-time streaming pipelines, and lakehouse architecture (Snowflake, Databricks, BigQuery).
- Establish master data management and data governance standards ensuring all systems — including those powering automated and predictive capabilities — are built on accurate, consistent, and trustworthy data.
- Own API strategy across enterprise systems and intelligent services: versioning standards, lifecycle management, security controls (OAuth 2.0, mTLS), and developer experience including interfaces for language model and agent-to-system communication.
- Establish and enforce technology standards, integration patterns, and usage policies — including deployment approvals, responsible use principles, explainability requirements, and bias monitoring for automated decision-making systems.
- Run formal governance processes for major platform changes and intelligent system releases: performance benchmarking, data lineage review, security sign-off, and stakeholder approval before production deployment.
- Define and track platform health metrics across enterprise and intelligent systems: uptime, API latency, integration error rates, model performance (accuracy, drift, latency), and technical debt — with regular reporting to technology leadership.
- Lead security architecture reviews ensuring all enterprise systems meet internal policy, data privacy regulation, and audit requirements.
- Champion operational excellence: alerting, monitoring, and incident response standards covering enterprise integrations and automated system behaviour in production.
- Mentor engineers and data practitioners, conduct architecture reviews, and build capability across technology and business teams — fostering a culture where intelligent tooling is a practical, well-governed asset.
- Collaborate with Project and Program Managers to align program scope with commercial budgets, timelines, and business priorities.
- Define success metrics for every program— including performance baselines, business impact KPIs, and operational cost benchmarks — with measurement frameworks live from go-live.
- Serve as the technical escalation point for complex system issues, integration failures, and automated system behavior concerns in production.
Required Qualifications
- 7+ years in enterprise solution architecture, technical consulting, or senior engineering leadership with a track record of large-scale system delivery.
- Proven hands-on experience designing and deploying intelligent or ML-driven solutions in production — including language model integration, retrieval-augmented generation, or model operationalization within enterprise environments.
- Solid experience integrating across two or more enterprise platforms: ERP (SAP, Oracle, NetSuite), CRM (Salesforce, Microsoft Dynamics), OMS, PIM, eCommerce, and data warehouses.
- Deep knowledge of enterprise integration patterns: REST and GraphQL APIs, event-driven architecture, message queuing, and iPaaS platforms (MuleSoft, Boomi, or similar).
- Practical experience with cloud intelligence services (AWS Bedrock, Azure OpenAI, Google Vertex AI) and direct integration with foundation model providers.
- Familiarity with orchestration frameworks, vector databases, and prompt engineering practices at production scale.
- Strong command of cloud infrastructure (AWS, GCP, or Azure), containerisation (Docker / Kubernetes), and CI/CD pipeline design.
- Demonstrated ability to translate business strategy into technology roadmaps and communicate trade-offs clearly to C-suite and board-level audiences.
- Experience producing high-quality documentation: BRDs, SDDs, ADRs, capability maps, and executive-level presentations.
- Experience building agentic systems: multi-agent orchestration, tool-use patterns, and human-in-the-loop workflow design.
- Background in consumer products, retail, or eCommerce with exposure to use cases such as demand forecasting, personalisation engines, or intelligent customer service.
- Experience with MLOps platforms and production model monitoring for drift, bias, and performance degradation.
- Familiarity with the EU AI Act and responsible-use principles including explainability, fairness, and auditability.
- Experience with TOGAF, Zachman, or another enterprise architecture framework.
- Relevant certifications: AWS Solutions Architect Professional, Google Professional Cloud Architect, Azure Solutions Architect Expert, TOGAF 9/10, or an ML specialist certification.
- Remote Flexibility – Work from anywhere while staying connected to a high-performing national sales team (Chicago based ideal).
- Generous PTO – Plus two floating holidays to use as you choose.
- 401(k) Match – Company contributions to support your long-term financial goals.
- Employee Product Perks – Hands-on access to ZAGG’s full portfolio of industry-leading products.
- Growth Culture – Join a B2B team with strong year-over-year momentum and a clear path to expanded responsibility.
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