Data and AI Governance Architect

Suddath Companies•Jacksonville, FL

3d•Hybrid

About The Position

The Data Governance & AI Readiness Architect serves as Suddath’s enterprise subject matter expert and functional leader for data services, governance execution, data quality, reporting, analytics, and AI-ready data operations within the Innovation team. This role leads the Enterprise Data team while advising business and corporate services stakeholders on enterprise data standards, trusted data assets, and the data readiness required to support analytics, automation, machine learning, and responsible AI. The role leads Suddath’s evolution toward a more governed, business-aligned, Microsoft Fabric-enabled, and AI-ready operating model. The position balances the reliability of current data services with modernization of data platforms, semantic models, governance practices, and business-facing data products. This leader is responsible for advancing Microsoft Fabric capabilities, optimizing DataOps through AI and automation, and managing relationships with Suddath’s businesses and corporate services, Microsoft and Microsoft partners, systems integrators, and specialist data and AI firms. The position translates enterprise data strategy into team objectives, delivery commitments, operational standards, measurable outcomes, and sustainable internal capabilities.

Requirements

Bachelor’s Degree (ideally in Computer Science, Information Systems, Data Management, or related field)
6+ years of data experience, including 2+ years in a leadership role
Strong expertise in data governance, quality, metadata, stewardship, architecture, and AI readiness
Experience with Microsoft Fabric, Power BI, Azure data platforms, and modern DataOps practices
Experience using AI and automation to improve data quality, monitoring, reliability, and operational efficiency
Experience managing Microsoft partners, system integrators, vendors, and internal stakeholders
Experience leading data work through Kanban or Agile practices
Strong communication, prioritization, team leadership, business partnership, and project management skills
Ability to read and interpret policies, standards, technical documentation, statements of work, architecture materials, and procedure manuals.
Ability to write standards, roadmaps, service updates, executive communications, operating procedures, and partner deliverables.
Ability to speak effectively with team members, business partners, technical specialists, external partners, and senior leadership.
Ability to apply quantitative reasoning to data quality, reliability, capacity, cost, service performance, prioritization, and operational outcomes.
Ability to interpret trends, performance indicators, workload metrics, quality measures, and value signals to guide decisions.
Ability to solve complex enterprise data problems involving governance, quality, architecture, AI-enabled operations, prioritization, risk, and service-delivery tradeoffs.
Ability to assess partner recommendations and technical alternatives using business value, reliability, security, governance, cost, supportability, and strategic fit.
Ability to interpret instructions provided in written, oral, diagram, architecture, workflow, or schedule form.
Strong working knowledge of enterprise data and reporting platforms, including Microsoft Fabric, Power BI, Microsoft SQL Server, Azure SQL, SSIS, SSRS, SSAS, Azure Data Gateway, Azure DevOps, source control, Microsoft Office, and Microsoft Visio, along with the ability to learn and effectively use technologies supporting data engineering, analytics, governance, AI-enabled DataOps, observability, automation, and service management.
Ability to prioritize and re-prioritize as enterprise needs, stakeholder demand, platform dependencies, incidents, partner commitments, and service expectations evolve.
Ability to manage team workload, modernization roadmaps, deadlines, budgets, partner deliverables, and service commitments while balancing governance, quality, reliability, and business value.
Direct supervisory responsibility for the Enterprise Data team, including performance management, hiring, coaching, talent development, succession planning, and workload prioritization.
Typically manages 3-5 direct reports across data engineering and analysis roles including contractors.

Nice To Haves

Microsoft Certified: Fabric Analytics Engineer Associate
Microsoft Certified: Power BI Data Analyst Associate
Microsoft Azure data-platform certification
CDMP or another DAMA-aligned data management or governance credential
PMP, ITIL, or comparable delivery and service-management certification

Responsibilities

Establish and operationalize enterprise standards for data ownership, stewardship, quality, metadata, lineage, access, and master and reference data.
Sustain a cross-business data owner and steward model with clear decision rights, operating cadences, and accountability for remediation.
Lead governance forums and report on priorities, risks, and decisions, partnering with cybersecurity, privacy, legal, and business leaders to mitigate data risk.
Define and enforce the readiness criteria that make enterprise data usable for analytics, automation, machine learning, Fabric Data Agents, and responsible AI, including structure, semantics, documentation, and approved-use guardrails developed with cybersecurity, privacy, and legal.
Guide the implementation, adoption, and governance of Microsoft Fabric, including data engineering patterns, workspaces, pipelines, semantic models, deployment practices, capacity management, and cost optimization.
Apply AI and automation across data operations to advance pipeline monitoring, anomaly detection, testing, documentation, root-cause analysis, predictive issue identification, and controlled remediation while maintaining appropriate human oversight.
Manage the Enterprise Data team, service priorities, major data-readiness and migration initiatives, and budgets.
Own engagements with Microsoft, Microsoft solution partners, and systems integrators end to end, holding them accountable for deliverables, knowledge transfer, budget, and performance, and converting their work into durable internal capability.